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"benchmark-results"]], "Bibliography": [[49, "bibliography"]], "Derivations": [[50, "derivations"]], "GPflow Documentation": [[51, "gpflow-documentation"]], "Intro:": [[51, null]], "Examples:": [[51, null]], "References:": [[51, null], [58, "References:"]], "Introduction": [[52, "introduction"]], "Install": [[52, "install"]], "Getting Started": [[52, "getting-started"]], "What\u2019s the difference between GPy and GPflow?": [[52, "what-s-the-difference-between-gpy-and-gpflow"]], "What models are implemented?": [[52, "what-models-are-implemented"]], "Regression": [[52, "regression"]], "MCMC": [[52, "mcmc"]], "Variational inference": [[52, "variational-inference"]], "Interdomain inference and multioutput GPs": [[52, "interdomain-inference-and-multioutput-gps"]], "GPLVM": [[52, "gplvm"]], "Benchmarks": [[52, "benchmarks"]], "Contributing": [[52, "contributing"]], "Citing GPflow": [[52, "citing-gpflow"]], "Acknowledgements": [[52, "acknowledgements"]], "GPflow manual": [[53, "gpflow-manual"]], "Basics": [[53, "basics"]], "Understanding": [[53, "understanding"], [84, "understanding"]], "Advanced needs": [[53, "advanced-needs"]], "Models": [[53, "models"]], "Features": [[53, "features"]], "Tailored models": [[53, "tailored-models"]], "Theoretical notes": [[53, "theoretical-notes"]], "Change points": [[54, "Change-points"]], "Convolutional Gaussian Processes": [[55, "Convolutional-Gaussian-Processes"]], "Generate dataset": [[55, "Generate-dataset"]], "Squared Exponential kernel": [[55, "Squared-Exponential-kernel"]], "Convolutional kernel": [[55, "Convolutional-kernel"]], "Conclusion": [[55, "Conclusion"]], "A simple demonstration of coregionalization": [[56, "A-simple-demonstration-of-coregionalization"]], "Data preparation": [[56, "Data-preparation"]], "Data formatting for the coregionalized model": [[56, "Data-formatting-for-the-coregionalized-model"]], "Building the coregionalization kernel": [[56, "Building-the-coregionalization-kernel"]], "Constructing the model": [[56, "Constructing-the-model"]], "References": [[56, "References"], [68, "References"], [69, "References"], [73, "References"], [76, "References"], [78, "References"]], "Faster predictions by caching": [[57, "Faster-predictions-by-caching"]], "Posterior predictive distribution": [[57, "Posterior-predictive-distribution"]], "What can be cached?": [[57, "What-can-be-cached?"]], "GPR Example": [[57, "GPR-Example"]], "SVGP Example": [[57, "SVGP-Example"]], "SGPR Example": [[57, "SGPR-Example"]], "Stochastic Variational Inference for scalability with SVGP": [[58, "Stochastic-Variational-Inference-for-scalability-with-SVGP"]], "Generating data": [[58, "Generating-data"]], "Building the model": [[58, "Building-the-model"]], "Likelihood computation: batch vs.\u00a0minibatch": [[58, "Likelihood-computation:-batch-vs.\u00a0minibatch"]], "Stochastical estimation of ELBO": [[58, "Stochastical-estimation-of-ELBO"]], "Minibatches speed up computation": [[58, "Minibatches-speed-up-computation"]], "Running stochastic optimization": [[58, "Running-stochastic-optimization"]], "Further reading": [[58, "Further-reading"], [59, "Further-reading"], [67, "Further-reading"], [69, "Further-reading"], [71, "Further-reading"]], "Heteroskedastic Likelihood and Multi-Latent GP": [[59, "Heteroskedastic-Likelihood-and-Multi-Latent-GP"]], "Standard (Homoskedastic) Regression": [[59, "Standard-(Homoskedastic)-Regression"]], "Heteroskedastic Regression": [[59, "Heteroskedastic-Regression"]], "Data Generation": [[59, "Data-Generation"]], "Plot Data": [[59, "Plot-Data"]], "Build Model": [[59, "Build-Model"]], "Likelihood": [[59, "Likelihood"]], "Kernel": [[59, "Kernel"]], "Inducing Points": [[59, "Inducing-Points"]], "SVGP Model": [[59, "SVGP-Model"]], "Model Optimization": [[59, "Model-Optimization"]], "Build Optimizers (NatGrad + Adam)": [[59, "Build-Optimizers-(NatGrad-+-Adam)"]], "Run Optimization Loop": [[59, "Run-Optimization-Loop"]], "Manipulating kernels": [[60, "Manipulating-kernels"]], "Standard kernels in GPflow": [[60, "Standard-kernels-in-GPflow"]], "First example: create a Matern 3/2 covariance kernel": [[60, "First-example:-create-a-Matern-3/2-covariance-kernel"]], "Combine kernels": [[60, "Combine-kernels"]], "Kernels for higher-dimensional input spaces": [[60, "Kernels-for-higher-dimensional-input-spaces"]], "Specify active dimensions": [[60, "Specify-active-dimensions"]], "Define new covariance functions": [[60, "Define-new-covariance-functions"]], "MCMC (Markov Chain Monte Carlo)": [[61, "MCMC-(Markov-Chain-Monte-Carlo)"]], "Example 1: GP regression": [[61, "Example-1:-GP-regression"]], "Data for a one-dimensional regression problem": [[61, "Data-for-a-one-dimensional-regression-problem"]], "MCMC for hyperparameters \\theta": [[61, "MCMC-for-hyperparameters-\\theta"]], "Example 2: Sparse MC for multiclass classification": [[61, "Example-2:-Sparse-MC-for-multiclass-classification"]], "Example 3: Fully Bayesian inference for generalized GP models with HMC": [[61, "Example-3:-Fully-Bayesian-inference-for-generalized-GP-models-with-HMC"]], "Exponential Regression": [[61, "Exponential-Regression"]], "Prior on constrained and unconstrained parameters": [[61, "Prior-on-constrained-and-unconstrained-parameters"]], "Multiclass classification": [[62, "Multiclass-classification"]], "Sampling from the GP multiclass generative model": [[62, "Sampling-from-the-GP-multiclass-generative-model"]], "Declaring model parameters and input": [[62, "Declaring-model-parameters-and-input"]], "Sampling": [[62, "Sampling"]], "Plotting": [[62, "Plotting"]], "Inference": [[62, "Inference"]], "Approximate inference: Sparse Variational Gaussian Process": [[62, "Approximate-inference:-Sparse-Variational-Gaussian-Process"]], "Declaring the SVGP model (see GPs for big data)": [[62, "Declaring-the-SVGP-model-(see-GPs-for-big-data)"]], "Running inference": [[62, "Running-inference"]], "Multi-output Gaussian processes in GPflow": [[63, "Multi-output-Gaussian-processes-in-GPflow"]], "Task": [[63, "Task"]], "Generate synthetic data": [[63, "Generate-synthetic-data"]], "Model the outputs of f(x) directly": [[63, "Model-the-outputs-of-f(x)-directly"]], "1. Shared independent multi-output kernel (MOK) and shared independent inducing variables": [[63, "1.-Shared-independent-multi-output-kernel-(MOK)-and-shared-independent-inducing-variables"]], "2. Separate independent MOK and shared independent inducing variables": [[63, "2.-Separate-independent-MOK-and-shared-independent-inducing-variables"]], "3. Separate independent kernel and separate independent inducing variables": [[63, "3.-Separate-independent-kernel-and-separate-independent-inducing-variables"]], "Model f(x) by doing inference in the g space": [[63, "Model-f(x)-by-doing-inference-in-the-g-space"]], "Mixed kernel and uncorrelated inducing variables": [[63, "Mixed-kernel-and-uncorrelated-inducing-variables"]], "Illustration of GPflow\u2019s multi-output capabilities": [[63, "Illustration-of-GPflow\u2019s-multi-output-capabilities"]], "Multi-output kernels (MOK) class diagram": [[63, "Multi-output-kernels-(MOK)-class-diagram"]], "Multi-output inducing variables class diagram": [[63, "Multi-output-inducing-variables-class-diagram"]], "Inducing points": [[63, "Inducing-points"]], "Fallback shared/separate independent inducing variables": [[63, "Fallback-shared/separate-independent-inducing-variables"]], "Shared/separate independent inducing variables": [[63, "Shared/separate-independent-inducing-variables"]], "Implemented combinations": [[63, "Implemented-combinations"]], "Debugging: introspect": [[63, "Debugging:-introspect"]], "Further Reading:": [[63, "Further-Reading:"]], "Natural gradients": [[64, "Natural-gradients"]], "VGP is a GPR": [[64, "VGP-is-a-GPR"]], "Optimize both variational parameters and kernel hyperparameters together": [[64, "Optimize-both-variational-parameters-and-kernel-hyperparameters-together"]], "Natural gradients also work for the sparse model": [[64, "Natural-gradients-also-work-for-the-sparse-model"]], "Minibatches": [[64, "Minibatches"]], "Comparison with ordinary gradients in the conjugate case": [[64, "Comparison-with-ordinary-gradients-in-the-conjugate-case"]], "Comparison with ordinary gradients in the non-conjugate case": [[64, "Comparison-with-ordinary-gradients-in-the-non-conjugate-case"]], "Binary classification": [[64, "Binary-classification"]], "Ordinal regression": [[65, "Ordinal-regression"]], "Variational Fourier Features in the GPflow framework": [[66, "Variational-Fourier-Features-in-the-GPflow-framework"]], "Gaussian process regression with varying output noise": [[67, "Gaussian-process-regression-with-varying-output-noise"]], "Demo 1: known noise variances": [[67, "Demo-1:-known-noise-variances"]], "Generate data": [[67, "Generate-data"], [67, "id1"], [67, "id2"]], "Try a naive fit": [[67, "Try-a-naive-fit"]], "Fit a polynomial to the noise scale": [[67, "Fit-a-polynomial-to-the-noise-scale"]], "Demo 2: grouped noise variances": [[67, "Demo-2:-grouped-noise-variances"]], "Fit a naive model": [[67, "Fit-a-naive-model"], [67, "id4"]], "Data structure": [[67, "Data-structure"], [67, "id3"]], "Use multiple functions for the noise variance": [[67, "Use-multiple-functions-for-the-noise-variance"]], "Demo 3: Empirical noise variance": [[67, "Demo-3:-Empirical-noise-variance"]], "Create custom function for the noise variance": [[67, "Create-custom-function-for-the-noise-variance"]], "Bayesian Gaussian process latent variable model (Bayesian GPLVM)": [[68, "Bayesian-Gaussian-process-latent-variable-model-(Bayesian-GPLVM)"]], "Data": [[68, "Data"]], "Model construction": [[68, "Model-construction"]], "Model analysis": [[68, "Model-analysis"]], "Plotting vs.\u00a0Principle Component Analysis (PCA)": [[68, "Plotting-vs.\u00a0Principle-Component-Analysis-(PCA)"]], "Basic (binary) GP classification model": [[69, "Basic-(binary)-GP-classification-model"]], "One-dimensional example": [[69, "One-dimensional-example"]], "Reminders on GP classification": [[69, "Reminders-on-GP-classification"]], "Implementation with GPflow": [[69, "Implementation-with-GPflow"]], "Predictions": [[69, "Predictions"]], "Two-dimensional example": [[69, "Two-dimensional-example"]], "Monitoring Optimisation": [[70, "Monitoring-Optimisation"]], "Setup": [[70, "Setup"]], "Set up data and model": [[70, "Set-up-data-and-model"]], "Set up monitoring tasks": [[70, "Set-up-monitoring-tasks"]], "For optimal performance, we can also wrap the monitor call inside tf.function:": [[70, "For-optimal-performance,-we-can-also-wrap-the-monitor-call-inside-tf.function:"]], "Scipy Optimization monitoring": [[70, "Scipy-Optimization-monitoring"]], "Basic (Gaussian likelihood) GP regression model": [[71, "Basic-(Gaussian-likelihood)-GP-regression-model"]], "Choose a kernel": [[71, "Choose-a-kernel"]], "Choose a mean function (optional)": [[71, "Choose-a-mean-function-(optional)"]], "Construct a model": [[71, "Construct-a-model"]], "Optimize the model parameters": [[71, "Optimize-the-model-parameters"]], "Make predictions": [[71, "Make-predictions"]], "GP regression in higher dimensions": [[71, "GP-regression-in-higher-dimensions"]], "GPflow with TensorFlow 2": [[72, "GPflow-with-TensorFlow-2"]], "Small steps big changes": [[72, "Small-steps-big-changes"]], "Loading data using TensorFlow Datasets": [[72, "Loading-data-using-TensorFlow-Datasets"]], "Define a GP model": [[72, "Define-a-GP-model"]], "Training using training_loss and training_loss_closure": [[72, "Training-using-training_loss-and-training_loss_closure"]], "Model-internal data": [[72, "Model-internal-data"]], "External data": [[72, "External-data"]], "Training using Gradient Tapes": [[72, "Training-using-Gradient-Tapes"]], "Monitoring": [[72, "Monitoring"]], "Saving and loading models": [[72, "Saving-and-loading-models"]], "Checkpointing": [[72, "Checkpointing"]], "Copying (hyper)parameter values between models": [[72, "Copying-(hyper)parameter-values-between-models"]], "TensorFlow saved_model": [[72, "TensorFlow-saved_model"]], "User config update": [[72, "User-config-update"]], "Custom mean functions: metalearning with GPs": [[73, "Custom-mean-functions:-metalearning-with-GPs"]], "Generate the tasks": [[73, "Generate-the-tasks"]], "Create the mean function": [[73, "Create-the-mean-function"]], "Build the GP metamodel": [[73, "Build-the-GP-metamodel"]], "Assess the model": [[73, "Assess-the-model"]], "Mixing TensorFlow models with GPflow": [[74, "Mixing-TensorFlow-models-with-GPflow"]], "Convolutional network inside a GPflow model": [[74, "Convolutional-network-inside-a-GPflow-model"]], "Kernel Design": [[75, "Kernel-Design"]], "__init__": [[75, "__init__"]], "K": [[75, "K"]], "K_diag": [[75, "K_diag"]], "Using the kernel in a model": [[75, "Using-the-kernel-in-a-model"]], "See also": [[75, "See-also"]], "Mixture Density Networks in GPflow": [[76, "Mixture-Density-Networks-in-GPflow"]], "Conditional Density Estimation models": [[76, "Conditional-Density-Estimation-models"]], "Mixture Density Network models": [[76, "Mixture-Density-Network-models"]], "A GPflow MDN implementation": [[76, "A-GPflow-MDN-implementation"]], "Notes": [[76, "Notes"]], "Experiment 1: The sinusoidal dataset": [[76, "Experiment-1:-The-sinusoidal-dataset"]], "Experiment 2: The half moon dataset": [[76, "Experiment-2:-The-half-moon-dataset"]], "Comparing FITC approximation to VFE approximation": [[77, "Comparing-FITC-approximation-to-VFE-approximation"]], "Derivation of SGPR equations": [[78, "Derivation-of-SGPR-equations"]], "Marginal likelihood bound": [[78, "Marginal-likelihood-bound"]], "Prediction": [[78, "Prediction"], [82, "Prediction"]], "Sanity checking when model behaviours should overlap": [[79, "Sanity-checking-when-model-behaviours-should-overlap"]], "Conjugate Gradient Lower Bound": [[80, "Conjugate-Gradient-Lower-Bound"]], "Discussion of the GP marginal likelihood upper bound": [[81, "Discussion-of-the-GP-marginal-likelihood-upper-bound"]], "Full model": [[81, "Full-model"]], "Upper bounds for sparse variational models": [[81, "Upper-bounds-for-sparse-variational-models"]], "Upper bounds for fixed hyperparameters": [[81, "Upper-bounds-for-fixed-hyperparameters"]], "A tight estimate bound does not imply a converged model": [[81, "A-tight-estimate-bound-does-not-imply-a-converged-model"]], "Derivation of VGP equations": [[82, "Derivation-of-VGP-equations"]], "Optimal distribution": [[82, "Optimal-distribution"]], "Marginals of q(\\mathbf f)": [[82, "Marginals-of-q(\\mathbf-f)"]], "KL divergence": [[82, "KL-divergence"]], "Manipulating GPflow models": [[83, "Manipulating-GPflow-models"]], "Viewing, getting, and setting parameters": [[83, "Viewing,-getting,-and-setting-parameters"]], "Constraints and trainable variables": [[83, "Constraints-and-trainable-variables"]], "Changing whether a parameter will be trained in optimization": [[83, "Changing-whether-a-parameter-will-be-trained-in-optimization"]], "Priors": [[83, "Priors"]], "Optimization": [[83, "Optimization"]], "Building new models": [[83, "Building-new-models"]], "Notebooks": [[84, "notebooks"]], "Basic": [[84, "basic"]], "Advanced": [[84, "advanced"]], "Tailor": [[84, "tailor"]], "Theory": [[84, "theory"]]}, "indexentries": {"prioron (class in gpflow.base)": [[0, "gpflow.base.PriorOn"]], "gpflow.base": [[0, "module-gpflow.base"]], "module": [[0, "module-gpflow.base"], [1, "module-gpflow.conditionals"], [2, "module-gpflow.conditionals.util"], [3, 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"gpflow.conditionals.multioutput.conditionals.fallback_independent_latent_conditional"]], "gpflow.conditionals": [[1, "module-gpflow.conditionals"]], "inducing_point_conditional() (in module gpflow.conditionals.multioutput.conditionals)": [[1, "gpflow.conditionals.multioutput.conditionals.inducing_point_conditional"]], "separate_independent_conditional() (in module gpflow.conditionals.multioutput.conditionals)": [[1, "gpflow.conditionals.multioutput.conditionals.separate_independent_conditional"]], "shared_independent_conditional() (in module gpflow.conditionals.multioutput.conditionals)": [[1, "gpflow.conditionals.multioutput.conditionals.shared_independent_conditional"]], "uncertain_conditional() (in module gpflow.conditionals)": [[1, "gpflow.conditionals.uncertain_conditional"]], "base_conditional_with_lm() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.base_conditional_with_lm"]], "expand_independent_outputs() (in module gpflow.conditionals.util)": [[2, 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"separate_independent_conditional_implementation() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.separate_independent_conditional_implementation"]], "config (class in gpflow.config)": [[3, "gpflow.config.Config"]], "as_context() (in module gpflow.config)": [[3, "gpflow.config.as_context"]], "config() (in module gpflow.config)": [[3, "gpflow.config.config"]], "default_positive_bijector() (in module gpflow.config)": [[3, "gpflow.config.default_positive_bijector"]], "default_positive_minimum() (in module gpflow.config)": [[3, "gpflow.config.default_positive_minimum"]], "default_summary_fmt() (in module gpflow.config)": [[3, "gpflow.config.default_summary_fmt"]], "float (gpflow.config.config attribute)": [[3, "gpflow.config.Config.float"]], "gpflow.config": [[3, "module-gpflow.config"]], "int (gpflow.config.config attribute)": [[3, "gpflow.config.Config.int"]], "jitter (gpflow.config.config attribute)": [[3, "gpflow.config.Config.jitter"]], "positive_bijector 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(gpflow.config.config attribute)": [[3, "gpflow.config.Config.summary_fmt"]], "kuf_conv_patch() (in module gpflow.covariances.kufs)": [[4, "gpflow.covariances.kufs.Kuf_conv_patch"]], "kuf_fallback_separate_linear_coregionalization() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_fallback_separate_linear_coregionalization"]], "kuf_fallback_shared_linear_coregionalization() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_fallback_shared_linear_coregionalization"]], "kuf_generic() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_generic"]], "kuf_kernel_inducingpoints() (in module gpflow.covariances.kufs)": [[4, "gpflow.covariances.kufs.Kuf_kernel_inducingpoints"]], "kuf_separate_linear_coregionalization() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_separate_linear_coregionalization"]], 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"gpflow.experimental.check_shapes.error_contexts.ObjectValueContext"]], "parallelcontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.ParallelContext"]], "parserinputcontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.ParserInputContext"]], "shapecontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.ShapeContext"]], "stackcontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.StackContext"]], "tensorspeccontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.TensorSpecContext"]], "variablecontext (class in gpflow.experimental.check_shapes.error_contexts)": [[12, "gpflow.experimental.check_shapes.error_contexts.VariableContext"]], 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(gpflow.experimental.check_shapes.error_contexts.argumentcontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.ArgumentContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.attributecontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.AttributeContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.conditioncontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.ConditionContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.functioncallcontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.FunctionCallContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.functiondefinitioncontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.FunctionDefinitionContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.indexcontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.IndexContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.larkunexpectedinputcontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.LarkUnexpectedInputContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.mappingkeycontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.MappingKeyContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.mappingvaluecontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.MappingValueContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.multipleelementboolcontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.MultipleElementBoolContext.print"]], "print() (gpflow.experimental.check_shapes.error_contexts.notecontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.NoteContext.print"]], "print() 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(gpflow.experimental.check_shapes.error_contexts.variablecontext method)": [[12, "gpflow.experimental.check_shapes.error_contexts.VariableContext.print"]], "argumentreferenceerror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.ArgumentReferenceError"]], "checkshapeserror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.CheckShapesError"]], "docstringparseerror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.DocstringParseError"]], "noshapeerror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.NoShapeError"]], "shapemismatcherror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.ShapeMismatchError"]], "specificationparseerror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.SpecificationParseError"]], "variabletypeerror (class in gpflow.experimental.check_shapes.exceptions)": [[13, "gpflow.experimental.check_shapes.exceptions.VariableTypeError"]], "gpflow.experimental.check_shapes.exceptions": [[13, "module-gpflow.experimental.check_shapes.exceptions"]], "disabled (gpflow.experimental.check_shapes.shapecheckingstate attribute)": [[14, "gpflow.experimental.check_shapes.ShapeCheckingState.DISABLED"]], "docstringformat (class in gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.DocstringFormat"]], "eager_mode_only (gpflow.experimental.check_shapes.shapecheckingstate attribute)": [[14, "gpflow.experimental.check_shapes.ShapeCheckingState.EAGER_MODE_ONLY"]], "enabled (gpflow.experimental.check_shapes.shapecheckingstate attribute)": [[14, "gpflow.experimental.check_shapes.ShapeCheckingState.ENABLED"]], "errorcontext (class in gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.ErrorContext"]], "none (gpflow.experimental.check_shapes.docstringformat attribute)": [[14, "gpflow.experimental.check_shapes.DocstringFormat.NONE"]], "sphinx (gpflow.experimental.check_shapes.docstringformat attribute)": [[14, "gpflow.experimental.check_shapes.DocstringFormat.SPHINX"]], "shapechecker (class in gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.ShapeChecker"]], "shapecheckingstate (class in gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.ShapeCheckingState"]], "add_context() (gpflow.experimental.check_shapes.shapechecker method)": [[14, "gpflow.experimental.check_shapes.ShapeChecker.add_context"]], "check_shape() (gpflow.experimental.check_shapes.shapechecker method)": [[14, "gpflow.experimental.check_shapes.ShapeChecker.check_shape"]], "check_shape() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.check_shape"]], "check_shapes() (gpflow.experimental.check_shapes.shapechecker method)": [[14, "gpflow.experimental.check_shapes.ShapeChecker.check_shapes"]], "check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.check_shapes"]], "disable_check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.disable_check_shapes"]], "get_check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_check_shapes"]], "get_enable_check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_enable_check_shapes"]], "get_enable_function_call_precompute() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_enable_function_call_precompute"]], "get_rewrite_docstrings() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_rewrite_docstrings"]], "get_shape() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_shape"]], "get_shape_checker() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.get_shape_checker"]], "gpflow.experimental.check_shapes": [[14, "module-gpflow.experimental.check_shapes"]], "inherit_check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.inherit_check_shapes"]], "print() (gpflow.experimental.check_shapes.errorcontext method)": [[14, "gpflow.experimental.check_shapes.ErrorContext.print"]], "register_get_shape() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.register_get_shape"]], "set_enable_check_shapes() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.set_enable_check_shapes"]], "set_enable_function_call_precompute() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.set_enable_function_call_precompute"]], "set_rewrite_docstrings() (in module gpflow.experimental.check_shapes)": [[14, "gpflow.experimental.check_shapes.set_rewrite_docstrings"]], "gpflow.experimental.check_shapes.parser": [[15, "module-gpflow.experimental.check_shapes.parser"]], "parse_and_rewrite_docstring() (in module gpflow.experimental.check_shapes.parser)": [[15, "gpflow.experimental.check_shapes.parser.parse_and_rewrite_docstring"]], "parse_function_spec() (in module gpflow.experimental.check_shapes.parser)": [[15, "gpflow.experimental.check_shapes.parser.parse_function_spec"]], "parse_tensor_spec() (in module gpflow.experimental.check_shapes.parser)": [[15, "gpflow.experimental.check_shapes.parser.parse_tensor_spec"]], "get_ndarray_shape() (in module gpflow.experimental.check_shapes.shapes)": [[16, "gpflow.experimental.check_shapes.shapes.get_ndarray_shape"]], "get_scalar_shape() (in module gpflow.experimental.check_shapes.shapes)": [[16, "gpflow.experimental.check_shapes.shapes.get_scalar_shape"]], "get_sequence_shape() (in module gpflow.experimental.check_shapes.shapes)": [[16, "gpflow.experimental.check_shapes.shapes.get_sequence_shape"]], "get_tensor_coercible_shape() (in module gpflow.experimental.check_shapes.shapes)": [[16, "gpflow.experimental.check_shapes.shapes.get_tensor_coercible_shape"]], "get_tensorflow_shape() (in module gpflow.experimental.check_shapes.shapes)": [[16, "gpflow.experimental.check_shapes.shapes.get_tensorflow_shape"]], "gpflow.experimental.check_shapes.shapes": [[16, "module-gpflow.experimental.check_shapes.shapes"]], "parsedargumentspec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedArgumentSpec"]], "parseddimensionspec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedDimensionSpec"]], "parsedfunctionspec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedFunctionSpec"]], "parsednotespec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedNoteSpec"]], "parsedshapespec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedShapeSpec"]], "parsedtensorspec (class in gpflow.experimental.check_shapes.specs)": [[17, "gpflow.experimental.check_shapes.specs.ParsedTensorSpec"]], "gpflow.experimental.check_shapes.specs": [[17, "module-gpflow.experimental.check_shapes.specs"]], "gpflow.experimental": [[18, "module-gpflow.experimental"]], "experimental() (in module gpflow.experimental.utils)": [[19, "gpflow.experimental.utils.experimental"]], "gpflow.experimental.utils": [[19, "module-gpflow.experimental.utils"]], "additive (class in gpflow.functions)": [[20, "gpflow.functions.Additive"]], "constant (class in gpflow.functions)": [[20, "gpflow.functions.Constant"]], "function (class in gpflow.functions)": [[20, "gpflow.functions.Function"]], "identity (class in gpflow.functions)": [[20, "gpflow.functions.Identity"]], "linear (class in gpflow.functions)": [[20, "gpflow.functions.Linear"]], "meanfunction (class in gpflow.functions)": [[20, "gpflow.functions.MeanFunction"]], "polynomial (class in gpflow.functions)": [[20, "gpflow.functions.Polynomial"]], "product (class in gpflow.functions)": [[20, "gpflow.functions.Product"]], "switchedfunction (class in gpflow.functions)": [[20, "gpflow.functions.SwitchedFunction"]], "switchedmeanfunction (class in gpflow.functions)": [[20, "gpflow.functions.SwitchedMeanFunction"]], "zero (class in gpflow.functions)": [[20, "gpflow.functions.Zero"]], "compute_powers() (gpflow.functions.polynomial static method)": [[20, "gpflow.functions.Polynomial.compute_powers"]], "gpflow.functions": [[20, "module-gpflow.functions"]], "module (class in gpflow)": [[21, "gpflow.Module"]], "parameter (class in gpflow)": [[21, "gpflow.Parameter"]], "assign() (gpflow.parameter method)": [[21, "gpflow.Parameter.assign"]], "default_float() (in module gpflow)": [[21, "gpflow.default_float"]], "default_int() (in module gpflow)": [[21, "gpflow.default_int"]], "default_jitter() (in module gpflow)": [[21, "gpflow.default_jitter"]], "gpflow": [[21, "module-gpflow"]], "log_prior_density() (gpflow.parameter method)": [[21, "gpflow.Parameter.log_prior_density"]], "set_trainable() (in module gpflow)": [[21, "gpflow.set_trainable"]], "trainable (gpflow.parameter property)": [[21, "gpflow.Parameter.trainable"]], "fallbackseparateindependentinducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables"]], "fallbacksharedindependentinducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables"]], "inducingpatches (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.InducingPatches"]], "inducingpoints (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.InducingPoints"]], "inducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.InducingVariables"]], "multioutputinducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.MultioutputInducingVariables"]], "multiscale (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.Multiscale"]], "separateindependentinducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.SeparateIndependentInducingVariables"]], "sharedindependentinducingvariables (class in gpflow.inducing_variables)": [[22, "gpflow.inducing_variables.SharedIndependentInducingVariables"]], "gpflow.inducing_variables": [[22, "module-gpflow.inducing_variables"]], "num_inducing (gpflow.inducing_variables.fallbackseparateindependentinducingvariables property)": [[22, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables.num_inducing"]], "num_inducing (gpflow.inducing_variables.fallbacksharedindependentinducingvariables property)": [[22, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables.num_inducing"]], "num_inducing (gpflow.inducing_variables.inducingvariables property)": [[22, "gpflow.inducing_variables.InducingVariables.num_inducing"]], "shape (gpflow.inducing_variables.fallbackseparateindependentinducingvariables property)": [[22, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables.shape"]], "shape (gpflow.inducing_variables.fallbacksharedindependentinducingvariables property)": [[22, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables.shape"]], "shape (gpflow.inducing_variables.inducingvariables property)": [[22, "gpflow.inducing_variables.InducingVariables.shape"]], "inducingpointsbase (class in gpflow.inducing_variables.inducing_variables)": [[23, "gpflow.inducing_variables.inducing_variables.InducingPointsBase"]], "get_scalar_shape() (in module gpflow.inducing_variables.inducing_variables)": [[23, "gpflow.inducing_variables.inducing_variables.get_scalar_shape"]], "gpflow.inducing_variables.inducing_variables": [[23, "module-gpflow.inducing_variables.inducing_variables"]], "num_inducing (gpflow.inducing_variables.inducing_variables.inducingpointsbase property)": [[23, "gpflow.inducing_variables.inducing_variables.InducingPointsBase.num_inducing"]], "shape (gpflow.inducing_variables.inducing_variables.inducingpointsbase property)": [[23, "gpflow.inducing_variables.inducing_variables.InducingPointsBase.shape"]], "reducingcombination (class in gpflow.kernels.base)": [[24, "gpflow.kernels.base.ReducingCombination"]], "gpflow.kernels.base": [[24, "module-gpflow.kernels.base"]], "anisotropicstationary (class in gpflow.kernels)": [[25, "gpflow.kernels.AnisotropicStationary"]], "arccosine (class in gpflow.kernels)": [[25, "gpflow.kernels.ArcCosine"]], "bias (in module gpflow.kernels)": [[25, "gpflow.kernels.Bias"]], "changepoints (class in gpflow.kernels)": [[25, "gpflow.kernels.ChangePoints"]], "combination (class in gpflow.kernels)": [[25, "gpflow.kernels.Combination"]], "convolutional (class in gpflow.kernels)": [[25, "gpflow.kernels.Convolutional"]], "coregion (class in gpflow.kernels)": [[25, "gpflow.kernels.Coregion"]], "cosine (class in gpflow.kernels)": [[25, "gpflow.kernels.Cosine"]], "exponential (class in gpflow.kernels)": [[25, "gpflow.kernels.Exponential"]], "independentlatent (class in gpflow.kernels)": [[25, "gpflow.kernels.IndependentLatent"]], "isotropicstationary (class in gpflow.kernels)": [[25, "gpflow.kernels.IsotropicStationary"]], "k() (gpflow.kernels.linearcoregionalization method)": [[25, "gpflow.kernels.LinearCoregionalization.K"]], "k() (gpflow.kernels.multioutputkernel method)": [[25, "gpflow.kernels.MultioutputKernel.K"]], "k() (gpflow.kernels.separateindependent method)": [[25, "gpflow.kernels.SeparateIndependent.K"]], "k() (gpflow.kernels.sharedindependent method)": [[25, "gpflow.kernels.SharedIndependent.K"]], "k_diag() (gpflow.kernels.linearcoregionalization method)": [[25, "gpflow.kernels.LinearCoregionalization.K_diag"]], "k_diag() (gpflow.kernels.multioutputkernel method)": [[25, "gpflow.kernels.MultioutputKernel.K_diag"]], "k_diag() (gpflow.kernels.separateindependent method)": [[25, "gpflow.kernels.SeparateIndependent.K_diag"]], "k_diag() (gpflow.kernels.sharedindependent method)": [[25, "gpflow.kernels.SharedIndependent.K_diag"]], "kernel (class in gpflow.kernels)": [[25, "gpflow.kernels.Kernel"]], "linear (class in gpflow.kernels)": [[25, "gpflow.kernels.Linear"]], "linearcoregionalization (class in gpflow.kernels)": [[25, "gpflow.kernels.LinearCoregionalization"]], "matern12 (class in gpflow.kernels)": [[25, "gpflow.kernels.Matern12"]], "matern32 (class in gpflow.kernels)": [[25, "gpflow.kernels.Matern32"]], "matern52 (class in gpflow.kernels)": [[25, "gpflow.kernels.Matern52"]], "multioutputkernel (class in gpflow.kernels)": [[25, "gpflow.kernels.MultioutputKernel"]], "periodic (class in gpflow.kernels)": [[25, "gpflow.kernels.Periodic"]], "polynomial (class in gpflow.kernels)": [[25, "gpflow.kernels.Polynomial"]], "product (class in gpflow.kernels)": [[25, "gpflow.kernels.Product"]], "rbf (in module gpflow.kernels)": [[25, "gpflow.kernels.RBF"]], "rationalquadratic (class in gpflow.kernels)": [[25, "gpflow.kernels.RationalQuadratic"]], "separateindependent (class in gpflow.kernels)": [[25, "gpflow.kernels.SeparateIndependent"]], "sharedindependent (class in gpflow.kernels)": [[25, "gpflow.kernels.SharedIndependent"]], "static (class in gpflow.kernels)": [[25, "gpflow.kernels.Static"]], "stationary (class in gpflow.kernels)": [[25, "gpflow.kernels.Stationary"]], "sum (class in gpflow.kernels)": [[25, "gpflow.kernels.Sum"]], "white (class in gpflow.kernels)": [[25, "gpflow.kernels.White"]], "ard (gpflow.kernels.arccosine property)": [[25, "gpflow.kernels.ArcCosine.ard"]], "ard (gpflow.kernels.linear property)": [[25, "gpflow.kernels.Linear.ard"]], "ard (gpflow.kernels.stationary property)": [[25, "gpflow.kernels.Stationary.ard"]], "get_patches() (gpflow.kernels.convolutional method)": [[25, "gpflow.kernels.Convolutional.get_patches"]], "gpflow.kernels": [[25, "module-gpflow.kernels"]], "latent_kernels (gpflow.kernels.linearcoregionalization property)": [[25, "gpflow.kernels.LinearCoregionalization.latent_kernels"]], "latent_kernels (gpflow.kernels.multioutputkernel property)": [[25, "gpflow.kernels.MultioutputKernel.latent_kernels"]], "latent_kernels (gpflow.kernels.separateindependent property)": [[25, "gpflow.kernels.SeparateIndependent.latent_kernels"]], "latent_kernels (gpflow.kernels.sharedindependent property)": [[25, "gpflow.kernels.SharedIndependent.latent_kernels"]], "num_latent_gps (gpflow.kernels.linearcoregionalization property)": [[25, "gpflow.kernels.LinearCoregionalization.num_latent_gps"]], "num_latent_gps (gpflow.kernels.multioutputkernel property)": [[25, "gpflow.kernels.MultioutputKernel.num_latent_gps"]], "num_latent_gps (gpflow.kernels.separateindependent property)": [[25, "gpflow.kernels.SeparateIndependent.num_latent_gps"]], "num_latent_gps (gpflow.kernels.sharedindependent property)": [[25, "gpflow.kernels.SharedIndependent.num_latent_gps"]], "on_separate_dimensions (gpflow.kernels.combination property)": [[25, "gpflow.kernels.Combination.on_separate_dimensions"]], "on_separate_dims() (gpflow.kernels.kernel method)": [[25, "gpflow.kernels.Kernel.on_separate_dims"]], "scaled_difference_matrix() (gpflow.kernels.anisotropicstationary method)": [[25, "gpflow.kernels.AnisotropicStationary.scaled_difference_matrix"]], "scaled_squared_euclid_dist() (gpflow.kernels.isotropicstationary method)": [[25, "gpflow.kernels.IsotropicStationary.scaled_squared_euclid_dist"]], "slice() (gpflow.kernels.kernel method)": [[25, "gpflow.kernels.Kernel.slice"]], "slice_cov() (gpflow.kernels.kernel method)": [[25, "gpflow.kernels.Kernel.slice_cov"]], "_() (in module gpflow.kullback_leiblers)": [[26, "gpflow.kullback_leiblers._"]], "gauss_kl() (in module gpflow.kullback_leiblers)": [[26, "gpflow.kullback_leiblers.gauss_kl"]], "gpflow.kullback_leiblers": [[26, "module-gpflow.kullback_leiblers"]], "bernoulli (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Bernoulli"]], "beta (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Beta"]], "exponential (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Exponential"]], "gamma (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Gamma"]], "gaussian (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Gaussian"]], "gaussianmc (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.GaussianMC"]], "heteroskedastictfpconditional (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.HeteroskedasticTFPConditional"]], "likelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Likelihood"]], "montecarlolikelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.MonteCarloLikelihood"]], "multiclass (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.MultiClass"]], "multilatentlikelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.MultiLatentLikelihood"]], "multilatenttfpconditional (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.MultiLatentTFPConditional"]], "ordinal (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Ordinal"]], "poisson (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Poisson"]], "quadraturelikelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.QuadratureLikelihood"]], "robustmax (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.RobustMax"]], "scalarlikelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.ScalarLikelihood"]], "softmax (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.Softmax"]], "studentt (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.StudentT"]], "switchedlikelihood (class in gpflow.likelihoods)": [[27, "gpflow.likelihoods.SwitchedLikelihood"]], "conditional_mean() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.conditional_mean"]], "conditional_variance() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.conditional_variance"]], "gpflow.likelihoods": [[27, "module-gpflow.likelihoods"]], "log_prob() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.log_prob"]], "predict_log_density() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.predict_log_density"]], "predict_mean_and_var() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.predict_mean_and_var"]], "variational_expectations() (gpflow.likelihoods.likelihood method)": [[27, "gpflow.likelihoods.Likelihood.variational_expectations"]], "gpflow.likelihoods.utils": [[28, "module-gpflow.likelihoods.utils"]], "inv_probit() (in module gpflow.likelihoods.utils)": [[28, "gpflow.likelihoods.utils.inv_probit"]], "bernoulli() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.bernoulli"]], "beta() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.beta"]], "exponential() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.exponential"]], "gamma() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.gamma"]], "gaussian() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.gaussian"]], "gpflow.logdensities": [[29, "module-gpflow.logdensities"]], "laplace() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.laplace"]], "lognormal() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.lognormal"]], "multivariate_normal() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.multivariate_normal"]], "poisson() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.poisson"]], "student_t() (in module gpflow.logdensities)": [[29, "gpflow.logdensities.student_t"]], "nystrompreconditioner (class in gpflow.models.cglb)": [[30, "gpflow.models.cglb.NystromPreconditioner"]], "cglb_conjugate_gradient() (in module gpflow.models.cglb)": [[30, "gpflow.models.cglb.cglb_conjugate_gradient"]], "gpflow.models.cglb": [[30, "module-gpflow.models.cglb"]], "gpr_deprecated (class in gpflow.models.gpr)": [[31, "gpflow.models.gpr.GPR_deprecated"]], "gpr_with_posterior (class in gpflow.models.gpr)": [[31, "gpflow.models.gpr.GPR_with_posterior"]], "gpflow.models.gpr": [[31, "module-gpflow.models.gpr"]], "log_marginal_likelihood() (gpflow.models.gpr.gpr_deprecated method)": [[31, "gpflow.models.gpr.GPR_deprecated.log_marginal_likelihood"]], "maximum_log_likelihood_objective() (gpflow.models.gpr.gpr_deprecated method)": [[31, "gpflow.models.gpr.GPR_deprecated.maximum_log_likelihood_objective"]], "posterior() (gpflow.models.gpr.gpr_with_posterior method)": [[31, "gpflow.models.gpr.GPR_with_posterior.posterior"]], "predict_f() (gpflow.models.gpr.gpr_deprecated method)": [[31, "gpflow.models.gpr.GPR_deprecated.predict_f"]], "predict_f() (gpflow.models.gpr.gpr_with_posterior method)": [[31, "gpflow.models.gpr.GPR_with_posterior.predict_f"]], "bayesiangplvm (class in gpflow.models)": [[32, "gpflow.models.BayesianGPLVM"]], "bayesianmodel (class in gpflow.models)": [[32, "gpflow.models.BayesianModel"]], "cglb (class in gpflow.models)": [[32, "gpflow.models.CGLB"]], "externaldatatraininglossmixin (class in gpflow.models)": [[32, "gpflow.models.ExternalDataTrainingLossMixin"]], "gplvm (class in gpflow.models)": [[32, "gpflow.models.GPLVM"]], "gpmc (class in gpflow.models)": [[32, "gpflow.models.GPMC"]], "gpmodel (class in gpflow.models)": [[32, "gpflow.models.GPModel"]], "gpr (class in gpflow.models)": [[32, "gpflow.models.GPR"]], "gprfitc (class in gpflow.models)": [[32, "gpflow.models.GPRFITC"]], "internaldatatraininglossmixin (class in gpflow.models)": [[32, "gpflow.models.InternalDataTrainingLossMixin"]], "sgpmc (class in gpflow.models)": [[32, "gpflow.models.SGPMC"]], "sgpr (class in gpflow.models)": [[32, "gpflow.models.SGPR"]], "svgp (class in gpflow.models)": [[32, "gpflow.models.SVGP"]], "vgp (class in gpflow.models)": [[32, "gpflow.models.VGP"]], "vgpopperarchambeau (class in gpflow.models)": [[32, "gpflow.models.VGPOpperArchambeau"]], "calc_num_latent_gps() (gpflow.models.gpmodel static method)": [[32, "gpflow.models.GPModel.calc_num_latent_gps"]], "calc_num_latent_gps_from_data() (gpflow.models.gpmodel static method)": [[32, "gpflow.models.GPModel.calc_num_latent_gps_from_data"]], "elbo() (gpflow.models.bayesiangplvm method)": [[32, "gpflow.models.BayesianGPLVM.elbo"]], "elbo() (gpflow.models.vgpopperarchambeau method)": [[32, "gpflow.models.VGPOpperArchambeau.elbo"]], "fitc_log_marginal_likelihood() (gpflow.models.gprfitc method)": [[32, "gpflow.models.GPRFITC.fitc_log_marginal_likelihood"]], "gpflow.models": [[32, "module-gpflow.models"]], "log_likelihood() (gpflow.models.gpmc method)": [[32, "gpflow.models.GPMC.log_likelihood"]], "log_likelihood_lower_bound() (gpflow.models.sgpmc method)": [[32, "gpflow.models.SGPMC.log_likelihood_lower_bound"]], "log_posterior_density() (gpflow.models.bayesianmodel method)": [[32, "gpflow.models.BayesianModel.log_posterior_density"]], "log_posterior_density() (gpflow.models.gpmc method)": [[32, "gpflow.models.GPMC.log_posterior_density"]], "log_posterior_density() (gpflow.models.sgpmc method)": [[32, "gpflow.models.SGPMC.log_posterior_density"]], "log_prior_density() (gpflow.models.bayesianmodel method)": [[32, "gpflow.models.BayesianModel.log_prior_density"]], "logdet_term() (gpflow.models.cglb method)": [[32, "gpflow.models.CGLB.logdet_term"]], "maximum_log_likelihood_objective() (gpflow.models.bayesiangplvm method)": [[32, "gpflow.models.BayesianGPLVM.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.bayesianmodel method)": [[32, "gpflow.models.BayesianModel.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.gpmc method)": [[32, "gpflow.models.GPMC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.gprfitc method)": [[32, "gpflow.models.GPRFITC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.sgpmc method)": [[32, "gpflow.models.SGPMC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.vgpopperarchambeau method)": [[32, "gpflow.models.VGPOpperArchambeau.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (in module gpflow.models)": [[32, "gpflow.models.maximum_log_likelihood_objective"]], "predict_f() (gpflow.models.bayesiangplvm method)": [[32, "gpflow.models.BayesianGPLVM.predict_f"]], "predict_f() (gpflow.models.cglb method)": [[32, "gpflow.models.CGLB.predict_f"]], "predict_f() 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method)": [[32, "gpflow.models.CGLB.quad_term"]], "training_loss() (gpflow.models.externaldatatraininglossmixin method)": [[32, "gpflow.models.ExternalDataTrainingLossMixin.training_loss"]], "training_loss() (gpflow.models.internaldatatraininglossmixin method)": [[32, "gpflow.models.InternalDataTrainingLossMixin.training_loss"]], "training_loss() (in module gpflow.models)": [[32, "gpflow.models.training_loss"]], "training_loss_closure() (gpflow.models.externaldatatraininglossmixin method)": [[32, "gpflow.models.ExternalDataTrainingLossMixin.training_loss_closure"]], "training_loss_closure() (gpflow.models.internaldatatraininglossmixin method)": [[32, "gpflow.models.InternalDataTrainingLossMixin.training_loss_closure"]], "training_loss_closure() (in module gpflow.models)": [[32, "gpflow.models.training_loss_closure"]], "a (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[33, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.A"]], "aat 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"gpflow.models.svgp.SVGP_with_posterior.posterior"]], "predict_f() (gpflow.models.svgp.svgp_with_posterior method)": [[34, "gpflow.models.svgp.SVGP_with_posterior.predict_f"]], "data_input_to_tensor() (in module gpflow.models.util)": [[35, "gpflow.models.util.data_input_to_tensor"]], "gpflow.models.util": [[35, "module-gpflow.models.util"]], "inducingpoint_wrapper() (in module gpflow.models.util)": [[35, "gpflow.models.util.inducingpoint_wrapper"]], "vgp_deprecated (class in gpflow.models.vgp)": [[36, "gpflow.models.vgp.VGP_deprecated"]], "vgp_with_posterior (class in gpflow.models.vgp)": [[36, "gpflow.models.vgp.VGP_with_posterior"]], "elbo() (gpflow.models.vgp.vgp_deprecated method)": [[36, "gpflow.models.vgp.VGP_deprecated.elbo"]], "gpflow.models.vgp": [[36, "module-gpflow.models.vgp"]], "maximum_log_likelihood_objective() (gpflow.models.vgp.vgp_deprecated method)": [[36, "gpflow.models.vgp.VGP_deprecated.maximum_log_likelihood_objective"]], "posterior() 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"xitransform (class in gpflow.optimizers)": [[38, "gpflow.optimizers.XiTransform"]], "convert_to_constrained_values() (gpflow.optimizers.samplinghelper method)": [[38, "gpflow.optimizers.SamplingHelper.convert_to_constrained_values"]], "current_state (gpflow.optimizers.samplinghelper property)": [[38, "gpflow.optimizers.SamplingHelper.current_state"]], "get_config() (gpflow.optimizers.naturalgradient method)": [[38, "gpflow.optimizers.NaturalGradient.get_config"]], "gpflow.optimizers": [[38, "module-gpflow.optimizers"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xinat static method)": [[38, "gpflow.optimizers.XiNat.meanvarsqrt_to_xi"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xisqrtmeanvar static method)": [[38, "gpflow.optimizers.XiSqrtMeanVar.meanvarsqrt_to_xi"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xitransform static method)": [[38, "gpflow.optimizers.XiTransform.meanvarsqrt_to_xi"]], "minimize() (gpflow.optimizers.naturalgradient method)": [[38, "gpflow.optimizers.NaturalGradient.minimize"]], "minimize() (gpflow.optimizers.scipy method)": [[38, "gpflow.optimizers.Scipy.minimize"]], "naturals_to_xi() (gpflow.optimizers.xinat static method)": [[38, "gpflow.optimizers.XiNat.naturals_to_xi"]], "naturals_to_xi() (gpflow.optimizers.xisqrtmeanvar static method)": [[38, "gpflow.optimizers.XiSqrtMeanVar.naturals_to_xi"]], "naturals_to_xi() (gpflow.optimizers.xitransform static method)": [[38, "gpflow.optimizers.XiTransform.naturals_to_xi"]], "target_log_prob_fn (gpflow.optimizers.samplinghelper property)": [[38, "gpflow.optimizers.SamplingHelper.target_log_prob_fn"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xinat static method)": [[38, "gpflow.optimizers.XiNat.xi_to_meanvarsqrt"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xisqrtmeanvar static method)": [[38, "gpflow.optimizers.XiSqrtMeanVar.xi_to_meanvarsqrt"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xitransform static method)": [[38, "gpflow.optimizers.XiTransform.xi_to_meanvarsqrt"]], "expectation_to_meanvarsqrt() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.expectation_to_meanvarsqrt"]], "expectation_to_natural() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.expectation_to_natural"]], "gpflow.optimizers.natgrad": [[39, "module-gpflow.optimizers.natgrad"]], "meanvarsqrt_to_expectation() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.meanvarsqrt_to_expectation"]], "meanvarsqrt_to_natural() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.meanvarsqrt_to_natural"]], "natural_to_expectation() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.natural_to_expectation"]], "natural_to_meanvarsqrt() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.natural_to_meanvarsqrt"]], "swap_dimensions() (in module gpflow.optimizers.natgrad)": [[39, "gpflow.optimizers.natgrad.swap_dimensions"]], "abstractposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.AbstractPosterior"]], "baseposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.BasePosterior"]], "fallbackindependentlatentposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.FallbackIndependentLatentPosterior"]], "fullycorrelatedposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.FullyCorrelatedPosterior"]], "gprposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.GPRPosterior"]], "independentposterior (class in gpflow.posteriors)": [[40, "gpflow.posteriors.IndependentPosterior"]], "independentposteriormultioutput (class in gpflow.posteriors)": [[40, "gpflow.posteriors.IndependentPosteriorMultiOutput"]], "independentposteriorsingleoutput (class in gpflow.posteriors)": [[40, "gpflow.posteriors.IndependentPosteriorSingleOutput"]], "linearcoregionalizationposterior (class in gpflow.posteriors)": [[40, 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"_get_posterior_linearcoregionalization_mo_efficient() (in module gpflow.posteriors)": [[40, "gpflow.posteriors._get_posterior_linearcoregionalization_mo_efficient"]], "axis_dynamic (gpflow.posteriors.precomputedvalue attribute)": [[40, "gpflow.posteriors.PrecomputedValue.axis_dynamic"]], "create_posterior() (in module gpflow.posteriors)": [[40, "gpflow.posteriors.create_posterior"]], "fused_predict_f() (gpflow.posteriors.abstractposterior method)": [[40, "gpflow.posteriors.AbstractPosterior.fused_predict_f"]], "get_precomputed_value_shape() (in module gpflow.posteriors)": [[40, "gpflow.posteriors.get_precomputed_value_shape"]], "gpflow.posteriors": [[40, "module-gpflow.posteriors"]], "predict_f() (gpflow.posteriors.abstractposterior method)": [[40, "gpflow.posteriors.AbstractPosterior.predict_f"]], "update_cache() (gpflow.posteriors.abstractposterior method)": [[40, "gpflow.posteriors.AbstractPosterior.update_cache"]], "value (gpflow.posteriors.precomputedvalue attribute)": [[40, "gpflow.posteriors.PrecomputedValue.value"]], "wrap_alpha_qinv() (gpflow.posteriors.precomputedvalue static method)": [[40, "gpflow.posteriors.PrecomputedValue.wrap_alpha_Qinv"]], "diagonalgaussian (class in gpflow.probability_distributions)": [[41, "gpflow.probability_distributions.DiagonalGaussian"]], "gaussian (class in gpflow.probability_distributions)": [[41, "gpflow.probability_distributions.Gaussian"]], "markovgaussian (class in gpflow.probability_distributions)": [[41, "gpflow.probability_distributions.MarkovGaussian"]], "probabilitydistribution (class in gpflow.probability_distributions)": [[41, "gpflow.probability_distributions.ProbabilityDistribution"]], "get_probability_distribution_shape() (in module gpflow.probability_distributions)": [[41, "gpflow.probability_distributions.get_probability_distribution_shape"]], "gpflow.probability_distributions": [[41, "module-gpflow.probability_distributions"]], "shape (gpflow.probability_distributions.diagonalgaussian property)": [[41, "gpflow.probability_distributions.DiagonalGaussian.shape"]], "shape (gpflow.probability_distributions.gaussian property)": [[41, "gpflow.probability_distributions.Gaussian.shape"]], "shape (gpflow.probability_distributions.markovgaussian property)": [[41, "gpflow.probability_distributions.MarkovGaussian.shape"]], "shape (gpflow.probability_distributions.probabilitydistribution property)": [[41, "gpflow.probability_distributions.ProbabilityDistribution.shape"]], "gh_points_and_weights() (in module gpflow.quadrature.gauss_hermite)": [[42, "gpflow.quadrature.gauss_hermite.gh_points_and_weights"]], "gpflow.quadrature.gauss_hermite": [[42, "module-gpflow.quadrature.gauss_hermite"]], "list_to_flat_grid() (in module gpflow.quadrature.gauss_hermite)": [[42, "gpflow.quadrature.gauss_hermite.list_to_flat_grid"]], "ndgh_points_and_weights() (in module gpflow.quadrature.gauss_hermite)": [[42, "gpflow.quadrature.gauss_hermite.ndgh_points_and_weights"]], "repeat_as_list() (in module 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"compute_mypy_flags() (in module gpflow.type_flags)": [[44, "gpflow.type_flags.compute_mypy_flags"]], "gpflow.type_flags": [[44, "module-gpflow.type_flags"]], "dispatcher (class in gpflow.utilities)": [[45, "gpflow.utilities.Dispatcher"]], "add_likelihood_noise_cov() (in module gpflow.utilities)": [[45, "gpflow.utilities.add_likelihood_noise_cov"]], "add_noise_cov() (in module gpflow.utilities)": [[45, "gpflow.utilities.add_noise_cov"]], "assert_params_false() (in module gpflow.utilities)": [[45, "gpflow.utilities.assert_params_false"]], "deepcopy() (in module gpflow.utilities)": [[45, "gpflow.utilities.deepcopy"]], "dispatch() (gpflow.utilities.dispatcher method)": [[45, "gpflow.utilities.Dispatcher.dispatch"]], "dispatch_or_raise() (gpflow.utilities.dispatcher method)": [[45, "gpflow.utilities.Dispatcher.dispatch_or_raise"]], "freeze() (in module gpflow.utilities)": [[45, "gpflow.utilities.freeze"]], "get_first_occurrence() (gpflow.utilities.dispatcher method)": [[45, 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gpflow.utilities)": [[45, "gpflow.utilities.select_dict_parameters_with_prior"]], "tabulate_module_summary() (in module gpflow.utilities)": [[45, "gpflow.utilities.tabulate_module_summary"]], "to_default_float() (in module gpflow.utilities)": [[45, "gpflow.utilities.to_default_float"]], "to_default_int() (in module gpflow.utilities)": [[45, "gpflow.utilities.to_default_int"]], "training_loop() (in module gpflow.utilities)": [[45, "gpflow.utilities.training_loop"]], "traverse_module() (in module gpflow.utilities)": [[45, "gpflow.utilities.traverse_module"]], "triangular() (in module gpflow.utilities)": [[45, "gpflow.utilities.triangular"]], "triangular_size() (in module gpflow.utilities)": [[45, "gpflow.utilities.triangular_size"]], "broadcasting_elementwise() (in module gpflow.utilities.ops)": [[46, "gpflow.utilities.ops.broadcasting_elementwise"]], "difference_matrix() (in module gpflow.utilities.ops)": [[46, "gpflow.utilities.ops.difference_matrix"]], "eye() (in module 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