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"Interdomain inference and multioutput GPs": [[40, "interdomain-inference-and-multioutput-gps"]], "GPLVM": [[40, "gplvm"]], "Heteroskedastic models": [[40, "heteroskedastic-models"]], "Contact": [[40, "contact"]], "Citing GPflow": [[40, "citing-gpflow"]], "Acknowledgements": [[40, "acknowledgements"]], "Installation": [[41, "installation"]], "Pre-built installation": [[41, "pre-built-installation"]], "Installation from source": [[41, "installation-from-source"]], "Versions": [[41, "versions"]], "Bayesian Gaussian process latent variable model (Bayesian GPLVM)": [[42, "Bayesian-Gaussian-process-latent-variable-model-(Bayesian-GPLVM)"]], "Data": [[42, "Data"]], "Model construction": [[42, "Model-construction"]], "Model analysis": [[42, "Model-analysis"]], "Plotting vs.\u00a0Principle Component Analysis (PCA)": [[42, "Plotting-vs.\u00a0Principle-Component-Analysis-(PCA)"]], "References": [[42, "References"], [45, "References"], [64, "References"], [67, "References"], [69, "References"]], "Change points": [[43, "Change-points"]], "Convolutional Gaussian Processes": [[44, "Convolutional-Gaussian-Processes"]], "Generate dataset": [[44, "Generate-dataset"]], "Squared Exponential kernel": [[44, "Squared-Exponential-kernel"]], "Convolutional kernel": [[44, "Convolutional-kernel"]], "Conclusion": [[44, "Conclusion"]], "A simple demonstration of coregionalization": [[45, "A-simple-demonstration-of-coregionalization"]], "Data preparation": [[45, "Data-preparation"]], "Data formatting for the coregionalized model": [[45, "Data-formatting-for-the-coregionalized-model"]], "Building the coregionalization kernel": [[45, "Building-the-coregionalization-kernel"]], "Constructing the model": [[45, "Constructing-the-model"]], "Faster predictions by caching": [[46, "Faster-predictions-by-caching"]], "Posterior predictive distribution": [[46, "Posterior-predictive-distribution"]], "What can be cached?": [[46, "What-can-be-cached?"]], "GPR Example": [[46, "GPR-Example"]], "SVGP Example": 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with HMC": [[49, "Example-3:-Fully-Bayesian-inference-for-generalized-GP-models-with-HMC"]], "Exponential Regression": [[49, "Exponential-Regression"]], "Prior on constrained and unconstrained parameters": [[49, "Prior-on-constrained-and-unconstrained-parameters"]], "Multiclass classification": [[50, "Multiclass-classification"]], "Sampling from the GP multiclass generative model": [[50, "Sampling-from-the-GP-multiclass-generative-model"]], "Declaring model parameters and input": [[50, "Declaring-model-parameters-and-input"]], "Sampling": [[50, "Sampling"]], "Plotting": [[50, "Plotting"]], "Inference": [[50, "Inference"]], "Approximate inference: Sparse Variational Gaussian Process": [[50, "Approximate-inference:-Sparse-Variational-Gaussian-Process"]], "Declaring the SVGP model (see GPs for big data)": [[50, "Declaring-the-SVGP-model-(see-GPs-for-big-data)"]], "Running inference": [[50, "Running-inference"]], "Multi-output Gaussian processes in GPflow": [[51, "Multi-output-Gaussian-processes-in-GPflow"]], "Task": [[51, "Task"]], "Generate synthetic data": [[51, "Generate-synthetic-data"]], "Model the outputs of f(x) directly": [[51, "Model-the-outputs-of-f(x)-directly"]], "1. Shared independent multi-output kernel (MOK) and shared independent inducing variables": [[51, "1.-Shared-independent-multi-output-kernel-(MOK)-and-shared-independent-inducing-variables"]], "2. Separate independent MOK and shared independent inducing variables": [[51, "2.-Separate-independent-MOK-and-shared-independent-inducing-variables"]], "3. Separate independent kernel and separate independent inducing variables": [[51, "3.-Separate-independent-kernel-and-separate-independent-inducing-variables"]], "Model f(x) by doing inference in the g space": [[51, "Model-f(x)-by-doing-inference-in-the-g-space"]], "Mixed kernel and uncorrelated inducing variables": [[51, "Mixed-kernel-and-uncorrelated-inducing-variables"]], "Illustration of GPflow\u2019s multi-output capabilities": [[51, "Illustration-of-GPflow\u2019s-multi-output-capabilities"]], "Multi-output kernels (MOK) class diagram": [[51, "Multi-output-kernels-(MOK)-class-diagram"]], "Multi-output inducing variables class diagram": [[51, "Multi-output-inducing-variables-class-diagram"]], "Inducing points": [[51, "Inducing-points"]], "Fallback shared/separate independent inducing variables": [[51, "Fallback-shared/separate-independent-inducing-variables"]], "Shared/separate independent inducing variables": [[51, "Shared/separate-independent-inducing-variables"]], "Implemented combinations": [[51, "Implemented-combinations"]], "Debugging: introspect": [[51, "Debugging:-introspect"]], "Further Reading:": [[51, "Further-Reading:"]], "Natural gradients": [[52, "Natural-gradients"]], "VGP is a GPR": [[52, "VGP-is-a-GPR"]], "Optimize both variational parameters and kernel hyperparameters together": [[52, "Optimize-both-variational-parameters-and-kernel-hyperparameters-together"]], "Natural gradients also work for the sparse model": [[52, "Natural-gradients-also-work-for-the-sparse-model"]], "Minibatches": [[52, "Minibatches"]], "Comparison with ordinary gradients in the conjugate case": [[52, "Comparison-with-ordinary-gradients-in-the-conjugate-case"]], "Comparison with ordinary gradients in the non-conjugate case": [[52, "Comparison-with-ordinary-gradients-in-the-non-conjugate-case"]], "Binary classification": [[52, "Binary-classification"]], "Ordinal regression": [[53, "Ordinal-regression"]], "Variational Fourier Features in the GPflow framework": [[54, "Variational-Fourier-Features-in-the-GPflow-framework"]], "Gaussian process regression with varying output noise": [[55, "Gaussian-process-regression-with-varying-output-noise"]], "Demo 1: known noise variances": [[55, "Demo-1:-known-noise-variances"]], "Generate data": [[55, "Generate-data"], [55, "id1"], [55, "id2"]], "Try a naive fit": [[55, "Try-a-naive-fit"]], "Fit a polynomial to the noise scale": [[55, "Fit-a-polynomial-to-the-noise-scale"]], "Demo 2: grouped noise variances": [[55, "Demo-2:-grouped-noise-variances"]], "Fit a naive model": [[55, "Fit-a-naive-model"], [55, "id4"]], "Data structure": [[55, "Data-structure"], [55, "id3"]], "Use multiple functions for the noise variance": [[55, "Use-multiple-functions-for-the-noise-variance"]], "Demo 3: Empirical noise variance": [[55, "Demo-3:-Empirical-noise-variance"]], "Create custom function for the noise variance": [[55, "Create-custom-function-for-the-noise-variance"]], "Basic Usage with GPR": [[56, "Basic-Usage-with-GPR"]], "Imports": [[56, "Imports"]], "Meet f and Y": [[56, "Meet-f-and-Y"]], "Minimal model": [[56, "Minimal-model"]], "Plotting the model": [[56, "Plotting-the-model"]], "Marginal variance vs full covariance": [[56, "Marginal-variance-vs-full-covariance"]], "What\u2019s next?": [[56, "What\u2019s-next?"]], "Classification, other data distributions, VGP and SVGP": [[57, "Classification,-other-data-distributions,-VGP-and-SVGP"]], "The Variational Gaussion Process": [[57, "The-Variational-Gaussion-Process"]], "Non-gaussian regression": [[57, "Non-gaussian-regression"]], "Classification": [[57, "Classification"]], "The Sparse Variational Gaussian Process": [[57, "The-Sparse-Variational-Gaussian-Process"]], "Writing code that handles both internal and external data models": [[57, "Writing-code-that-handles-both-internal-and-external-data-models"]], "Kernels": [[58, "Kernels"]], "What is a kernel?": [[58, "What-is-a-kernel?"]], "Visualising a kernel": [[58, "Visualising-a-kernel"]], "The Mat\u00e9rn family": [[58, "The-Mat\u00e9rn-family"]], "Kernel parameters": [[58, "Kernel-parameters"]], "Kernel composition": [[58, "Kernel-composition"]], "Periodic": [[58, "Periodic"]], "Change-points": [[58, "Change-points"]], "Some other simple kernels": [[58, "Some-other-simple-kernels"]], "Addition and multiplication": [[58, "Addition-and-multiplication"]], "Multiple-dimensional data": [[58, "Multiple-dimensional-data"]], "Multi-dimensional parameters": [[58, "Multi-dimensional-parameters"]], "Active dimensions": [[58, "Active-dimensions"]], "Large Data with SGPR": [[59, "Large-Data-with-SGPR"]], "Picking initial inducing points": [[59, "Picking-initial-inducing-points"]], "Random data samples": [[59, "Random-data-samples"]], "k-means": [[59, "k-means"]], "Uncorrelated samples": [[59, "Uncorrelated-samples"]], "Advanced initialisation": [[59, "Advanced-initialisation"]], "Mean Functions": [[60, "Mean-Functions"]], "Mean reversion": [[60, "Mean-reversion"]], "Setting a mean function": [[60, "Setting-a-mean-function"]], "Interactions with the kernel": [[60, "Interactions-with-the-kernel"]], "Kernels versus mean functions": [[60, "Kernels-versus-mean-functions"]], "Custom mean functions": [[60, "Custom-mean-functions"]], "Composition": [[60, "Composition"]], "Monitoring": [[61, "Monitoring"]], "Components of monitoring": [[61, "Components-of-monitoring"]], "ExecuteCallback": [[61, "ExecuteCallback"]], "TensorBoard integration": [[61, "TensorBoard-integration"]], "Parameters and Their Optimisation": [[62, "Parameters-and-Their-Optimisation"]], "The Module and Parameter classes": [[62, "The-Module-and-Parameter-classes"]], "Inspecting parameters": [[62, "Inspecting-parameters"]], "Setting parameters": [[62, "Setting-parameters"]], "Optimisation": [[62, "Optimisation"]], "What to do if you model fails to fit": [[62, "What-to-do-if-you-model-fails-to-fit"]], "Dataset: CO_2 levels": [[62, "Dataset:-CO_2-levels"]], "Setting the initial value": [[62, "Setting-the-initial-value"]], "Priors": [[62, "Priors"]], "Transforms": [[62, "Transforms"]], "Trainable parameters": [[62, "Trainable-parameters"]], "Advanced training": [[62, "Advanced-training"]], "Multi-stage training": [[62, "Multi-stage-training"]], "The Keras optimisers": [[62, "The-Keras-optimisers"]], "Saving and Loading Models": [[63, "Saving-and-Loading-Models"]], "Checkpointing": [[63, "Checkpointing"]], "TensorFlow saved_model": [[63, "TensorFlow-saved_model"]], "Copying (hyper)parameter values between models": [[63, "Copying-(hyper)parameter-values-between-models"]], "Which method to use": [[63, "Which-method-to-use"]], "Custom mean functions: metalearning with GPs": [[64, "Custom-mean-functions:-metalearning-with-GPs"]], "Generate the tasks": [[64, "Generate-the-tasks"]], "Create the mean function": [[64, "Create-the-mean-function"]], "Build the GP metamodel": [[64, "Build-the-GP-metamodel"]], "Assess the model": [[64, "Assess-the-model"]], "Mixing TensorFlow models with GPflow": [[65, "Mixing-TensorFlow-models-with-GPflow"]], "Convolutional network inside a GPflow model": [[65, "Convolutional-network-inside-a-GPflow-model"]], "Kernel Design": [[66, "Kernel-Design"]], "__init__": [[66, "__init__"]], "K": [[66, "K"]], "K_diag": [[66, "K_diag"]], "Using the kernel in a model": [[66, "Using-the-kernel-in-a-model"]], "See also": [[66, "See-also"]], "Mixture Density Networks in GPflow": [[67, "Mixture-Density-Networks-in-GPflow"]], "Conditional Density Estimation models": [[67, "Conditional-Density-Estimation-models"]], "Mixture Density Network models": [[67, "Mixture-Density-Network-models"]], "A GPflow MDN implementation": [[67, "A-GPflow-MDN-implementation"]], "Notes": [[67, "Notes"]], "Experiment 1: The sinusoidal dataset": [[67, "Experiment-1:-The-sinusoidal-dataset"]], "Experiment 2: The half moon dataset": [[67, "Experiment-2:-The-half-moon-dataset"]], "Comparing FITC approximation to VFE approximation": [[68, "Comparing-FITC-approximation-to-VFE-approximation"]], "Derivation of SGPR equations": [[69, "Derivation-of-SGPR-equations"]], "Marginal likelihood bound": [[69, "Marginal-likelihood-bound"]], "Prediction": [[69, "Prediction"], [73, "Prediction"]], "Sanity checking when model behaviours should overlap": [[70, "Sanity-checking-when-model-behaviours-should-overlap"]], "Conjugate Gradient Lower Bound": [[71, "Conjugate-Gradient-Lower-Bound"]], "Discussion of the GP marginal likelihood upper bound": [[72, "Discussion-of-the-GP-marginal-likelihood-upper-bound"]], "Full model": [[72, "Full-model"]], "Upper bounds for sparse variational models": [[72, "Upper-bounds-for-sparse-variational-models"]], "Upper bounds for fixed hyperparameters": [[72, "Upper-bounds-for-fixed-hyperparameters"]], "A tight estimate bound does not imply a converged model": [[72, "A-tight-estimate-bound-does-not-imply-a-converged-model"]], "Derivation of VGP equations": [[73, "Derivation-of-VGP-equations"]], "Optimal distribution": [[73, "Optimal-distribution"]], "Marginals of q(\\mathbf f)": [[73, "Marginals-of-q(\\mathbf-f)"]], "KL divergence": [[73, "KL-divergence"]], "User Guide": [[74, "user-guide"]], "Theoretical notes": [[74, "theoretical-notes"]], "Tailored models": [[74, "tailored-models"]], "Advanced needs": [[74, "advanced-needs"]]}, "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, "module-gpflow.config"], [4, "module-gpflow.covariances"], [5, "module-gpflow.expectations"], [6, "module-gpflow.expectations.quadratures"], [7, "module-gpflow.experimental"], [8, "module-gpflow.experimental.utils"], [9, "module-gpflow.functions"], [10, "module-gpflow"], [11, "module-gpflow.inducing_variables"], [12, "module-gpflow.inducing_variables.inducing_variables"], [13, "module-gpflow.kernels.base"], [14, "module-gpflow.kernels"], [15, "module-gpflow.kullback_leiblers"], [16, "module-gpflow.likelihoods"], [17, "module-gpflow.likelihoods.utils"], [18, "module-gpflow.logdensities"], [19, "module-gpflow.models.cglb"], [20, "module-gpflow.models.gpr"], [21, "module-gpflow.models"], [22, "module-gpflow.models.sgpr"], [23, "module-gpflow.models.svgp"], [24, "module-gpflow.models.util"], [25, "module-gpflow.models.vgp"], [26, "module-gpflow.monitor"], [27, "module-gpflow.optimizers"], [28, "module-gpflow.optimizers.natgrad"], [29, "module-gpflow.posteriors"], [30, "module-gpflow.probability_distributions"], [31, "module-gpflow.quadrature.gauss_hermite"], [32, "module-gpflow.quadrature"], [33, "module-gpflow.type_flags"], [34, "module-gpflow.utilities"], [35, "module-gpflow.utilities.ops"], [36, "module-gpflow.utilities.parameter_or_function"]], "_dense_conditional() (in module gpflow.conditionals.conditionals)": [[1, "gpflow.conditionals.conditionals._dense_conditional"]], "_sample_conditional() (in module gpflow.conditionals.multioutput.sample_conditionals)": [[1, "gpflow.conditionals.multioutput.sample_conditionals._sample_conditional"]], "_sample_conditional() (in module gpflow.conditionals.sample_conditionals)": [[1, "gpflow.conditionals.sample_conditionals._sample_conditional"]], "_sparse_conditional() (in module gpflow.conditionals.conditionals)": [[1, "gpflow.conditionals.conditionals._sparse_conditional"]], "base_conditional() (in module gpflow.conditionals)": [[1, "gpflow.conditionals.base_conditional"]], "coregionalization_conditional() (in module gpflow.conditionals.multioutput.conditionals)": [[1, "gpflow.conditionals.multioutput.conditionals.coregionalization_conditional"]], "fallback_independent_latent_conditional() (in module gpflow.conditionals.multioutput.conditionals)": [[1, "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, "gpflow.conditionals.util.expand_independent_outputs"]], "fully_correlated_conditional() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.fully_correlated_conditional"]], "fully_correlated_conditional_repeat() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.fully_correlated_conditional_repeat"]], "gpflow.conditionals.util": [[2, "module-gpflow.conditionals.util"]], "independent_interdomain_conditional() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.independent_interdomain_conditional"]], "mix_latent_gp() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.mix_latent_gp"]], "rollaxis_left() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.rollaxis_left"]], "rollaxis_right() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.rollaxis_right"]], "sample_mvn() (in module gpflow.conditionals.util)": [[2, "gpflow.conditionals.util.sample_mvn"]], "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 (gpflow.config.config attribute)": [[3, "gpflow.config.Config.positive_bijector"]], "positive_bijector_type_map() (in module gpflow.config)": [[3, "gpflow.config.positive_bijector_type_map"]], "positive_minimum (gpflow.config.config attribute)": [[3, "gpflow.config.Config.positive_minimum"]], "set_config() (in module gpflow.config)": [[3, "gpflow.config.set_config"]], "set_default_float() (in module gpflow.config)": [[3, "gpflow.config.set_default_float"]], "set_default_int() (in module gpflow.config)": [[3, "gpflow.config.set_default_int"]], "set_default_jitter() (in module gpflow.config)": [[3, "gpflow.config.set_default_jitter"]], "set_default_positive_bijector() (in module gpflow.config)": [[3, "gpflow.config.set_default_positive_bijector"]], "set_default_positive_minimum() (in module gpflow.config)": [[3, "gpflow.config.set_default_positive_minimum"]], "set_default_summary_fmt() (in module gpflow.config)": [[3, "gpflow.config.set_default_summary_fmt"]], "summary_fmt (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"]], "kuf_separate_separate() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_separate_separate"]], "kuf_separate_shared() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_separate_shared"]], "kuf_shared_linear_coregionalization() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_shared_linear_coregionalization"]], "kuf_shared_separate() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_shared_separate"]], "kuf_shared_shared() (in module gpflow.covariances.multioutput.kufs)": [[4, "gpflow.covariances.multioutput.kufs.Kuf_shared_shared"]], "kuf_sqexp_multiscale() (in module gpflow.covariances.kufs)": [[4, "gpflow.covariances.kufs.Kuf_sqexp_multiscale"]], "kuu_conv_patch() (in module gpflow.covariances.kuus)": [[4, "gpflow.covariances.kuus.Kuu_conv_patch"]], "kuu_fallbace_separate() (in module gpflow.covariances.multioutput.kuus)": [[4, "gpflow.covariances.multioutput.kuus.Kuu_fallbace_separate"]], "kuu_fallback_separate_shared() (in module gpflow.covariances.multioutput.kuus)": [[4, "gpflow.covariances.multioutput.kuus.Kuu_fallback_separate_shared"]], "kuu_fallback_shared() (in module gpflow.covariances.multioutput.kuus)": [[4, "gpflow.covariances.multioutput.kuus.Kuu_fallback_shared"]], "kuu_generic() (in module gpflow.covariances.multioutput.kuus)": [[4, "gpflow.covariances.multioutput.kuus.Kuu_generic"]], "kuu_kernel_inducingpoints() (in module gpflow.covariances.kuus)": [[4, "gpflow.covariances.kuus.Kuu_kernel_inducingpoints"]], "kuu_shared_shared() (in module gpflow.covariances.multioutput.kuus)": [[4, "gpflow.covariances.multioutput.kuus.Kuu_shared_shared"]], "kuu_sqexp_multiscale() (in module gpflow.covariances.kuus)": [[4, "gpflow.covariances.kuus.Kuu_sqexp_multiscale"]], "gpflow.covariances": [[4, "module-gpflow.covariances"]], "expectation() (in module gpflow.expectations)": [[5, "gpflow.expectations.expectation"]], "gpflow.expectations": [[5, "module-gpflow.expectations"]], "quadrature_expectation() (in module gpflow.expectations)": [[5, "gpflow.expectations.quadrature_expectation"]], "get_eval_func() (in module gpflow.expectations.quadratures)": [[6, "gpflow.expectations.quadratures.get_eval_func"]], "gpflow.expectations.quadratures": [[6, "module-gpflow.expectations.quadratures"]], "gpflow.experimental": [[7, "module-gpflow.experimental"]], "experimental() (in module gpflow.experimental.utils)": [[8, "gpflow.experimental.utils.experimental"]], "gpflow.experimental.utils": [[8, "module-gpflow.experimental.utils"]], "additive (class in gpflow.functions)": [[9, "gpflow.functions.Additive"]], "constant (class in gpflow.functions)": [[9, "gpflow.functions.Constant"]], "function (class in gpflow.functions)": [[9, "gpflow.functions.Function"]], "identity (class in gpflow.functions)": [[9, "gpflow.functions.Identity"]], "linear (class in gpflow.functions)": [[9, "gpflow.functions.Linear"]], "meanfunction (class in gpflow.functions)": [[9, "gpflow.functions.MeanFunction"]], "polynomial (class in gpflow.functions)": [[9, "gpflow.functions.Polynomial"]], "product (class in gpflow.functions)": [[9, "gpflow.functions.Product"]], "switchedfunction (class in gpflow.functions)": [[9, "gpflow.functions.SwitchedFunction"]], "switchedmeanfunction (class in gpflow.functions)": [[9, "gpflow.functions.SwitchedMeanFunction"]], "zero (class in gpflow.functions)": [[9, "gpflow.functions.Zero"]], "compute_powers() (gpflow.functions.polynomial static method)": [[9, "gpflow.functions.Polynomial.compute_powers"]], "gpflow.functions": [[9, "module-gpflow.functions"]], "module (class in gpflow)": [[10, "gpflow.Module"]], "parameter (class in gpflow)": [[10, "gpflow.Parameter"]], "assign() (gpflow.parameter method)": [[10, "gpflow.Parameter.assign"]], "default_float() (in module gpflow)": [[10, "gpflow.default_float"]], "default_int() (in module gpflow)": [[10, "gpflow.default_int"]], "default_jitter() (in module gpflow)": [[10, "gpflow.default_jitter"]], "gpflow": [[10, "module-gpflow"]], "log_prior_density() (gpflow.parameter method)": [[10, "gpflow.Parameter.log_prior_density"]], "set_trainable() (in module gpflow)": [[10, "gpflow.set_trainable"]], "trainable (gpflow.parameter property)": [[10, "gpflow.Parameter.trainable"]], "fallbackseparateindependentinducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables"]], "fallbacksharedindependentinducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables"]], "inducingpatches (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.InducingPatches"]], "inducingpoints (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.InducingPoints"]], "inducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.InducingVariables"]], "multioutputinducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.MultioutputInducingVariables"]], "multiscale (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.Multiscale"]], "separateindependentinducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.SeparateIndependentInducingVariables"]], "sharedindependentinducingvariables (class in gpflow.inducing_variables)": [[11, "gpflow.inducing_variables.SharedIndependentInducingVariables"]], "gpflow.inducing_variables": [[11, "module-gpflow.inducing_variables"]], "num_inducing (gpflow.inducing_variables.fallbackseparateindependentinducingvariables property)": [[11, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables.num_inducing"]], "num_inducing (gpflow.inducing_variables.fallbacksharedindependentinducingvariables property)": [[11, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables.num_inducing"]], "num_inducing (gpflow.inducing_variables.inducingvariables property)": [[11, "gpflow.inducing_variables.InducingVariables.num_inducing"]], "shape (gpflow.inducing_variables.fallbackseparateindependentinducingvariables property)": [[11, "gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables.shape"]], "shape (gpflow.inducing_variables.fallbacksharedindependentinducingvariables property)": [[11, "gpflow.inducing_variables.FallbackSharedIndependentInducingVariables.shape"]], "shape (gpflow.inducing_variables.inducingvariables property)": [[11, "gpflow.inducing_variables.InducingVariables.shape"]], "inducingpointsbase (class in gpflow.inducing_variables.inducing_variables)": [[12, "gpflow.inducing_variables.inducing_variables.InducingPointsBase"]], "gpflow.inducing_variables.inducing_variables": [[12, "module-gpflow.inducing_variables.inducing_variables"]], "num_inducing (gpflow.inducing_variables.inducing_variables.inducingpointsbase property)": [[12, "gpflow.inducing_variables.inducing_variables.InducingPointsBase.num_inducing"]], "shape (gpflow.inducing_variables.inducing_variables.inducingpointsbase property)": [[12, "gpflow.inducing_variables.inducing_variables.InducingPointsBase.shape"]], "reducingcombination (class in gpflow.kernels.base)": [[13, "gpflow.kernels.base.ReducingCombination"]], "gpflow.kernels.base": [[13, "module-gpflow.kernels.base"]], "anisotropicstationary (class in gpflow.kernels)": [[14, "gpflow.kernels.AnisotropicStationary"]], "arccosine (class in gpflow.kernels)": [[14, "gpflow.kernels.ArcCosine"]], "bias (in module gpflow.kernels)": [[14, "gpflow.kernels.Bias"]], "changepoints (class in gpflow.kernels)": [[14, "gpflow.kernels.ChangePoints"]], "combination (class in gpflow.kernels)": [[14, "gpflow.kernels.Combination"]], "convolutional (class in gpflow.kernels)": [[14, "gpflow.kernels.Convolutional"]], "coregion (class in gpflow.kernels)": [[14, "gpflow.kernels.Coregion"]], "cosine (class in gpflow.kernels)": [[14, "gpflow.kernels.Cosine"]], "exponential (class in gpflow.kernels)": [[14, "gpflow.kernels.Exponential"]], "independentlatent (class in gpflow.kernels)": [[14, "gpflow.kernels.IndependentLatent"]], "isotropicstationary (class in gpflow.kernels)": [[14, "gpflow.kernels.IsotropicStationary"]], "k() (gpflow.kernels.linearcoregionalization method)": [[14, "gpflow.kernels.LinearCoregionalization.K"]], "k() (gpflow.kernels.multioutputkernel method)": [[14, "gpflow.kernels.MultioutputKernel.K"]], "k() (gpflow.kernels.separateindependent method)": [[14, "gpflow.kernels.SeparateIndependent.K"]], "k() (gpflow.kernels.sharedindependent method)": [[14, "gpflow.kernels.SharedIndependent.K"]], "k_diag() (gpflow.kernels.linearcoregionalization method)": [[14, "gpflow.kernels.LinearCoregionalization.K_diag"]], "k_diag() (gpflow.kernels.multioutputkernel method)": [[14, "gpflow.kernels.MultioutputKernel.K_diag"]], "k_diag() (gpflow.kernels.separateindependent method)": [[14, "gpflow.kernels.SeparateIndependent.K_diag"]], "k_diag() (gpflow.kernels.sharedindependent method)": [[14, "gpflow.kernels.SharedIndependent.K_diag"]], "kernel (class in gpflow.kernels)": [[14, "gpflow.kernels.Kernel"]], "linear (class in gpflow.kernels)": [[14, "gpflow.kernels.Linear"]], "linearcoregionalization (class in gpflow.kernels)": [[14, "gpflow.kernels.LinearCoregionalization"]], "matern12 (class in gpflow.kernels)": [[14, "gpflow.kernels.Matern12"]], "matern32 (class in gpflow.kernels)": [[14, "gpflow.kernels.Matern32"]], "matern52 (class in gpflow.kernels)": [[14, "gpflow.kernels.Matern52"]], "multioutputkernel (class in gpflow.kernels)": [[14, "gpflow.kernels.MultioutputKernel"]], "periodic (class in gpflow.kernels)": [[14, "gpflow.kernels.Periodic"]], "polynomial (class in gpflow.kernels)": [[14, "gpflow.kernels.Polynomial"]], "product (class in gpflow.kernels)": [[14, "gpflow.kernels.Product"]], "rbf (in module gpflow.kernels)": [[14, "gpflow.kernels.RBF"]], "rationalquadratic (class in gpflow.kernels)": [[14, "gpflow.kernels.RationalQuadratic"]], "separateindependent (class in gpflow.kernels)": [[14, "gpflow.kernels.SeparateIndependent"]], "sharedindependent (class in gpflow.kernels)": [[14, "gpflow.kernels.SharedIndependent"]], "static (class in gpflow.kernels)": [[14, "gpflow.kernels.Static"]], "stationary (class in gpflow.kernels)": [[14, "gpflow.kernels.Stationary"]], "sum (class in gpflow.kernels)": [[14, "gpflow.kernels.Sum"]], "white (class in gpflow.kernels)": [[14, "gpflow.kernels.White"]], "ard (gpflow.kernels.arccosine property)": [[14, "gpflow.kernels.ArcCosine.ard"]], "ard (gpflow.kernels.linear property)": [[14, "gpflow.kernels.Linear.ard"]], "ard (gpflow.kernels.stationary property)": [[14, "gpflow.kernels.Stationary.ard"]], "get_patches() (gpflow.kernels.convolutional method)": [[14, "gpflow.kernels.Convolutional.get_patches"]], "gpflow.kernels": [[14, "module-gpflow.kernels"]], "latent_kernels (gpflow.kernels.linearcoregionalization property)": [[14, "gpflow.kernels.LinearCoregionalization.latent_kernels"]], "latent_kernels (gpflow.kernels.multioutputkernel property)": [[14, "gpflow.kernels.MultioutputKernel.latent_kernels"]], "latent_kernels (gpflow.kernels.separateindependent property)": [[14, "gpflow.kernels.SeparateIndependent.latent_kernels"]], "latent_kernels (gpflow.kernels.sharedindependent property)": [[14, "gpflow.kernels.SharedIndependent.latent_kernels"]], "num_latent_gps (gpflow.kernels.linearcoregionalization property)": [[14, "gpflow.kernels.LinearCoregionalization.num_latent_gps"]], "num_latent_gps (gpflow.kernels.multioutputkernel property)": [[14, "gpflow.kernels.MultioutputKernel.num_latent_gps"]], "num_latent_gps (gpflow.kernels.separateindependent property)": [[14, "gpflow.kernels.SeparateIndependent.num_latent_gps"]], "num_latent_gps (gpflow.kernels.sharedindependent property)": [[14, "gpflow.kernels.SharedIndependent.num_latent_gps"]], "on_separate_dimensions (gpflow.kernels.combination property)": [[14, "gpflow.kernels.Combination.on_separate_dimensions"]], "on_separate_dims() (gpflow.kernels.kernel method)": [[14, "gpflow.kernels.Kernel.on_separate_dims"]], "scaled_difference_matrix() (gpflow.kernels.anisotropicstationary method)": [[14, "gpflow.kernels.AnisotropicStationary.scaled_difference_matrix"]], "scaled_squared_euclid_dist() (gpflow.kernels.isotropicstationary method)": [[14, "gpflow.kernels.IsotropicStationary.scaled_squared_euclid_dist"]], "slice() (gpflow.kernels.kernel method)": [[14, "gpflow.kernels.Kernel.slice"]], "slice_cov() (gpflow.kernels.kernel method)": [[14, "gpflow.kernels.Kernel.slice_cov"]], "_() (in module gpflow.kullback_leiblers)": [[15, "gpflow.kullback_leiblers._"]], "gauss_kl() (in module gpflow.kullback_leiblers)": [[15, "gpflow.kullback_leiblers.gauss_kl"]], "gpflow.kullback_leiblers": [[15, "module-gpflow.kullback_leiblers"]], "bernoulli (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Bernoulli"]], "beta (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Beta"]], "exponential (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Exponential"]], "gamma (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Gamma"]], "gaussian (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Gaussian"]], "gaussianmc (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.GaussianMC"]], "heteroskedastictfpconditional (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.HeteroskedasticTFPConditional"]], "likelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Likelihood"]], "montecarlolikelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.MonteCarloLikelihood"]], "multiclass (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.MultiClass"]], "multilatentlikelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.MultiLatentLikelihood"]], "multilatenttfpconditional (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.MultiLatentTFPConditional"]], "ordinal (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Ordinal"]], "poisson (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Poisson"]], "quadraturelikelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.QuadratureLikelihood"]], "robustmax (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.RobustMax"]], "scalarlikelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.ScalarLikelihood"]], "softmax (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.Softmax"]], "studentt (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.StudentT"]], "switchedlikelihood (class in gpflow.likelihoods)": [[16, "gpflow.likelihoods.SwitchedLikelihood"]], "conditional_mean() (gpflow.likelihoods.likelihood method)": [[16, 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module gpflow.logdensities)": [[18, "gpflow.logdensities.beta"]], "exponential() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.exponential"]], "gamma() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.gamma"]], "gaussian() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.gaussian"]], "gpflow.logdensities": [[18, "module-gpflow.logdensities"]], "laplace() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.laplace"]], "lognormal() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.lognormal"]], "multivariate_normal() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.multivariate_normal"]], "poisson() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.poisson"]], "student_t() (in module gpflow.logdensities)": [[18, "gpflow.logdensities.student_t"]], "nystrompreconditioner (class in gpflow.models.cglb)": [[19, "gpflow.models.cglb.NystromPreconditioner"]], "cglb_conjugate_gradient() (in module 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"gpflow.models.SGPMC.log_likelihood_lower_bound"]], "log_posterior_density() (gpflow.models.bayesianmodel method)": [[21, "gpflow.models.BayesianModel.log_posterior_density"]], "log_posterior_density() (gpflow.models.gpmc method)": [[21, "gpflow.models.GPMC.log_posterior_density"]], "log_posterior_density() (gpflow.models.sgpmc method)": [[21, "gpflow.models.SGPMC.log_posterior_density"]], "log_prior_density() (gpflow.models.bayesianmodel method)": [[21, "gpflow.models.BayesianModel.log_prior_density"]], "logdet_term() (gpflow.models.cglb method)": [[21, "gpflow.models.CGLB.logdet_term"]], "maximum_log_likelihood_objective() (gpflow.models.bayesiangplvm method)": [[21, "gpflow.models.BayesianGPLVM.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.bayesianmodel method)": [[21, "gpflow.models.BayesianModel.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.gpmc method)": [[21, "gpflow.models.GPMC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.gprfitc method)": [[21, "gpflow.models.GPRFITC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.sgpmc method)": [[21, "gpflow.models.SGPMC.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (gpflow.models.vgpopperarchambeau method)": [[21, "gpflow.models.VGPOpperArchambeau.maximum_log_likelihood_objective"]], "maximum_log_likelihood_objective() (in module gpflow.models)": [[21, "gpflow.models.maximum_log_likelihood_objective"]], "predict_f() (gpflow.models.bayesiangplvm method)": [[21, "gpflow.models.BayesianGPLVM.predict_f"]], "predict_f() (gpflow.models.cglb method)": [[21, "gpflow.models.CGLB.predict_f"]], "predict_f() (gpflow.models.gpmc method)": [[21, "gpflow.models.GPMC.predict_f"]], "predict_f() (gpflow.models.gpmodel method)": [[21, "gpflow.models.GPModel.predict_f"]], "predict_f() (gpflow.models.gprfitc method)": [[21, "gpflow.models.GPRFITC.predict_f"]], "predict_f() (gpflow.models.sgpmc method)": [[21, "gpflow.models.SGPMC.predict_f"]], "predict_f() (gpflow.models.vgpopperarchambeau method)": [[21, "gpflow.models.VGPOpperArchambeau.predict_f"]], "predict_f_samples() (gpflow.models.gpmodel method)": [[21, "gpflow.models.GPModel.predict_f_samples"]], "predict_log_density() (gpflow.models.bayesiangplvm method)": [[21, "gpflow.models.BayesianGPLVM.predict_log_density"]], "predict_log_density() (gpflow.models.cglb method)": [[21, "gpflow.models.CGLB.predict_log_density"]], "predict_log_density() (gpflow.models.gpmodel method)": [[21, "gpflow.models.GPModel.predict_log_density"]], "predict_y() (gpflow.models.cglb method)": [[21, "gpflow.models.CGLB.predict_y"]], "predict_y() (gpflow.models.gpmodel method)": [[21, "gpflow.models.GPModel.predict_y"]], "quad_term() (gpflow.models.cglb method)": [[21, "gpflow.models.CGLB.quad_term"]], "training_loss() (gpflow.models.externaldatatraininglossmixin method)": [[21, "gpflow.models.ExternalDataTrainingLossMixin.training_loss"]], "training_loss() (gpflow.models.internaldatatraininglossmixin method)": [[21, "gpflow.models.InternalDataTrainingLossMixin.training_loss"]], "training_loss() (in module gpflow.models)": [[21, "gpflow.models.training_loss"]], "training_loss_closure() (gpflow.models.externaldatatraininglossmixin method)": [[21, "gpflow.models.ExternalDataTrainingLossMixin.training_loss_closure"]], "training_loss_closure() (gpflow.models.internaldatatraininglossmixin method)": [[21, "gpflow.models.InternalDataTrainingLossMixin.training_loss_closure"]], "training_loss_closure() (in module gpflow.models)": [[21, "gpflow.models.training_loss_closure"]], "a (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.A"]], "aat (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.AAT"]], "b (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.B"]], "l (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.L"]], "lb (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.LB"]], "sgprbase_deprecated (class in gpflow.models.sgpr)": [[22, "gpflow.models.sgpr.SGPRBase_deprecated"]], "sgpr_deprecated (class in gpflow.models.sgpr)": [[22, "gpflow.models.sgpr.SGPR_deprecated"]], "sgpr_deprecated.commontensors (class in gpflow.models.sgpr)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors"]], "sgpr_with_posterior (class in gpflow.models.sgpr)": [[22, "gpflow.models.sgpr.SGPR_with_posterior"]], "compute_qu() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.compute_qu"]], "elbo() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.elbo"]], "gpflow.models.sgpr": [[22, "module-gpflow.models.sgpr"]], "logdet_term() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.logdet_term"]], "maximum_log_likelihood_objective() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.maximum_log_likelihood_objective"]], "posterior() (gpflow.models.sgpr.sgpr_with_posterior method)": [[22, "gpflow.models.sgpr.SGPR_with_posterior.posterior"]], "predict_f() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.predict_f"]], "predict_f() (gpflow.models.sgpr.sgpr_with_posterior method)": [[22, "gpflow.models.sgpr.SGPR_with_posterior.predict_f"]], "quad_term() (gpflow.models.sgpr.sgpr_deprecated method)": [[22, "gpflow.models.sgpr.SGPR_deprecated.quad_term"]], "sigma (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.sigma"]], "sigma_sq (gpflow.models.sgpr.sgpr_deprecated.commontensors attribute)": [[22, "gpflow.models.sgpr.SGPR_deprecated.CommonTensors.sigma_sq"]], "upper_bound() (gpflow.models.sgpr.sgprbase_deprecated method)": [[22, "gpflow.models.sgpr.SGPRBase_deprecated.upper_bound"]], "svgp_deprecated (class in gpflow.models.svgp)": [[23, "gpflow.models.svgp.SVGP_deprecated"]], "svgp_with_posterior (class in gpflow.models.svgp)": [[23, "gpflow.models.svgp.SVGP_with_posterior"]], "elbo() (gpflow.models.svgp.svgp_deprecated method)": [[23, "gpflow.models.svgp.SVGP_deprecated.elbo"]], "gpflow.models.svgp": [[23, "module-gpflow.models.svgp"]], "maximum_log_likelihood_objective() (gpflow.models.svgp.svgp_deprecated method)": [[23, "gpflow.models.svgp.SVGP_deprecated.maximum_log_likelihood_objective"]], "posterior() (gpflow.models.svgp.svgp_with_posterior method)": [[23, "gpflow.models.svgp.SVGP_with_posterior.posterior"]], "predict_f() (gpflow.models.svgp.svgp_deprecated method)": [[23, "gpflow.models.svgp.SVGP_deprecated.predict_f"]], "predict_f() (gpflow.models.svgp.svgp_with_posterior method)": [[23, "gpflow.models.svgp.SVGP_with_posterior.predict_f"]], "data_input_to_tensor() (in module gpflow.models.util)": [[24, "gpflow.models.util.data_input_to_tensor"]], "gpflow.models.util": [[24, "module-gpflow.models.util"]], "inducingpoint_wrapper() (in module gpflow.models.util)": [[24, "gpflow.models.util.inducingpoint_wrapper"]], "vgp_deprecated (class in gpflow.models.vgp)": [[25, "gpflow.models.vgp.VGP_deprecated"]], "vgp_with_posterior (class in gpflow.models.vgp)": [[25, "gpflow.models.vgp.VGP_with_posterior"]], "elbo() (gpflow.models.vgp.vgp_deprecated method)": [[25, "gpflow.models.vgp.VGP_deprecated.elbo"]], "gpflow.models.vgp": [[25, "module-gpflow.models.vgp"]], "maximum_log_likelihood_objective() (gpflow.models.vgp.vgp_deprecated method)": [[25, "gpflow.models.vgp.VGP_deprecated.maximum_log_likelihood_objective"]], "posterior() (gpflow.models.vgp.vgp_with_posterior method)": [[25, "gpflow.models.vgp.VGP_with_posterior.posterior"]], "predict_f() (gpflow.models.vgp.vgp_deprecated method)": [[25, "gpflow.models.vgp.VGP_deprecated.predict_f"]], "predict_f() (gpflow.models.vgp.vgp_with_posterior method)": [[25, "gpflow.models.vgp.VGP_with_posterior.predict_f"]], "update_vgp_data() (in module gpflow.models.vgp)": [[25, "gpflow.models.vgp.update_vgp_data"]], "executecallback (class in gpflow.monitor)": [[26, "gpflow.monitor.ExecuteCallback"]], "imagetotensorboard (class in gpflow.monitor)": [[26, "gpflow.monitor.ImageToTensorBoard"]], "modeltotensorboard (class in gpflow.monitor)": [[26, "gpflow.monitor.ModelToTensorBoard"]], "monitor (class in gpflow.monitor)": [[26, "gpflow.monitor.Monitor"]], "monitortask (class in gpflow.monitor)": [[26, "gpflow.monitor.MonitorTask"]], "monitortaskgroup (class in gpflow.monitor)": [[26, "gpflow.monitor.MonitorTaskGroup"]], "scalartotensorboard (class in gpflow.monitor)": [[26, "gpflow.monitor.ScalarToTensorBoard"]], "totensorboard (class in gpflow.monitor)": [[26, "gpflow.monitor.ToTensorBoard"]], "gpflow.monitor": [[26, "module-gpflow.monitor"]], "run() (gpflow.monitor.executecallback method)": [[26, "gpflow.monitor.ExecuteCallback.run"]], "run() (gpflow.monitor.imagetotensorboard method)": [[26, "gpflow.monitor.ImageToTensorBoard.run"]], "run() (gpflow.monitor.modeltotensorboard method)": [[26, "gpflow.monitor.ModelToTensorBoard.run"]], "run() (gpflow.monitor.monitortask method)": [[26, "gpflow.monitor.MonitorTask.run"]], "run() (gpflow.monitor.scalartotensorboard method)": [[26, "gpflow.monitor.ScalarToTensorBoard.run"]], "naturalgradient (class in gpflow.optimizers)": [[27, "gpflow.optimizers.NaturalGradient"]], "samplinghelper (class in gpflow.optimizers)": [[27, "gpflow.optimizers.SamplingHelper"]], "scipy (class in gpflow.optimizers)": [[27, "gpflow.optimizers.Scipy"]], "xinat (class in gpflow.optimizers)": [[27, "gpflow.optimizers.XiNat"]], "xisqrtmeanvar (class in gpflow.optimizers)": [[27, "gpflow.optimizers.XiSqrtMeanVar"]], "xitransform (class in gpflow.optimizers)": [[27, "gpflow.optimizers.XiTransform"]], "convert_to_constrained_values() (gpflow.optimizers.samplinghelper method)": [[27, "gpflow.optimizers.SamplingHelper.convert_to_constrained_values"]], "current_state (gpflow.optimizers.samplinghelper property)": [[27, "gpflow.optimizers.SamplingHelper.current_state"]], "get_config() (gpflow.optimizers.naturalgradient method)": [[27, "gpflow.optimizers.NaturalGradient.get_config"]], "gpflow.optimizers": [[27, "module-gpflow.optimizers"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xinat static method)": [[27, "gpflow.optimizers.XiNat.meanvarsqrt_to_xi"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xisqrtmeanvar static method)": [[27, "gpflow.optimizers.XiSqrtMeanVar.meanvarsqrt_to_xi"]], "meanvarsqrt_to_xi() (gpflow.optimizers.xitransform static method)": [[27, "gpflow.optimizers.XiTransform.meanvarsqrt_to_xi"]], "minimize() (gpflow.optimizers.naturalgradient method)": [[27, "gpflow.optimizers.NaturalGradient.minimize"]], "minimize() (gpflow.optimizers.scipy method)": [[27, "gpflow.optimizers.Scipy.minimize"]], "naturals_to_xi() (gpflow.optimizers.xinat static method)": [[27, "gpflow.optimizers.XiNat.naturals_to_xi"]], "naturals_to_xi() (gpflow.optimizers.xisqrtmeanvar static method)": [[27, "gpflow.optimizers.XiSqrtMeanVar.naturals_to_xi"]], "naturals_to_xi() (gpflow.optimizers.xitransform static method)": [[27, "gpflow.optimizers.XiTransform.naturals_to_xi"]], "target_log_prob_fn (gpflow.optimizers.samplinghelper property)": [[27, "gpflow.optimizers.SamplingHelper.target_log_prob_fn"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xinat static method)": [[27, "gpflow.optimizers.XiNat.xi_to_meanvarsqrt"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xisqrtmeanvar static method)": [[27, "gpflow.optimizers.XiSqrtMeanVar.xi_to_meanvarsqrt"]], "xi_to_meanvarsqrt() (gpflow.optimizers.xitransform static method)": [[27, "gpflow.optimizers.XiTransform.xi_to_meanvarsqrt"]], "expectation_to_meanvarsqrt() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.expectation_to_meanvarsqrt"]], "expectation_to_natural() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.expectation_to_natural"]], "gpflow.optimizers.natgrad": [[28, "module-gpflow.optimizers.natgrad"]], "meanvarsqrt_to_expectation() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.meanvarsqrt_to_expectation"]], "meanvarsqrt_to_natural() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.meanvarsqrt_to_natural"]], "natural_to_expectation() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.natural_to_expectation"]], "natural_to_meanvarsqrt() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.natural_to_meanvarsqrt"]], "swap_dimensions() (in module gpflow.optimizers.natgrad)": [[28, "gpflow.optimizers.natgrad.swap_dimensions"]], "abstractposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.AbstractPosterior"]], "baseposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.BasePosterior"]], "fallbackindependentlatentposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.FallbackIndependentLatentPosterior"]], "fullycorrelatedposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.FullyCorrelatedPosterior"]], "gprposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.GPRPosterior"]], "independentposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.IndependentPosterior"]], "independentposteriormultioutput (class in gpflow.posteriors)": [[29, "gpflow.posteriors.IndependentPosteriorMultiOutput"]], "independentposteriorsingleoutput (class in gpflow.posteriors)": [[29, "gpflow.posteriors.IndependentPosteriorSingleOutput"]], "linearcoregionalizationposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.LinearCoregionalizationPosterior"]], "precomputecachetype (class in gpflow.posteriors)": [[29, "gpflow.posteriors.PrecomputeCacheType"]], "precomputedvalue (class in gpflow.posteriors)": [[29, "gpflow.posteriors.PrecomputedValue"]], "sgprposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.SGPRPosterior"]], "vgpposterior (class in gpflow.posteriors)": [[29, "gpflow.posteriors.VGPPosterior"]], "_get_posterior_base_case() (in module gpflow.posteriors)": [[29, "gpflow.posteriors._get_posterior_base_case"]], "_get_posterior_fully_correlated_mo() (in module gpflow.posteriors)": [[29, "gpflow.posteriors._get_posterior_fully_correlated_mo"]], "_get_posterior_independent_mo() (in module gpflow.posteriors)": [[29, "gpflow.posteriors._get_posterior_independent_mo"]], "_get_posterior_independentlatent_mo_fallback() (in module gpflow.posteriors)": [[29, "gpflow.posteriors._get_posterior_independentlatent_mo_fallback"]], "_get_posterior_linearcoregionalization_mo_efficient() (in module gpflow.posteriors)": [[29, "gpflow.posteriors._get_posterior_linearcoregionalization_mo_efficient"]], "axis_dynamic (gpflow.posteriors.precomputedvalue attribute)": [[29, "gpflow.posteriors.PrecomputedValue.axis_dynamic"]], "create_posterior() (in module gpflow.posteriors)": [[29, "gpflow.posteriors.create_posterior"]], "fused_predict_f() (gpflow.posteriors.abstractposterior method)": [[29, "gpflow.posteriors.AbstractPosterior.fused_predict_f"]], "get_precomputed_value_shape() (in module gpflow.posteriors)": [[29, "gpflow.posteriors.get_precomputed_value_shape"]], "gpflow.posteriors": [[29, "module-gpflow.posteriors"]], "predict_f() (gpflow.posteriors.abstractposterior method)": [[29, "gpflow.posteriors.AbstractPosterior.predict_f"]], "update_cache() (gpflow.posteriors.abstractposterior method)": [[29, "gpflow.posteriors.AbstractPosterior.update_cache"]], "value (gpflow.posteriors.precomputedvalue attribute)": [[29, "gpflow.posteriors.PrecomputedValue.value"]], "wrap_alpha_qinv() (gpflow.posteriors.precomputedvalue static method)": [[29, "gpflow.posteriors.PrecomputedValue.wrap_alpha_Qinv"]], "diagonalgaussian (class in gpflow.probability_distributions)": [[30, "gpflow.probability_distributions.DiagonalGaussian"]], "gaussian (class in gpflow.probability_distributions)": [[30, "gpflow.probability_distributions.Gaussian"]], "markovgaussian (class in gpflow.probability_distributions)": [[30, "gpflow.probability_distributions.MarkovGaussian"]], "probabilitydistribution (class in gpflow.probability_distributions)": [[30, "gpflow.probability_distributions.ProbabilityDistribution"]], "get_probability_distribution_shape() (in module gpflow.probability_distributions)": [[30, "gpflow.probability_distributions.get_probability_distribution_shape"]], "gpflow.probability_distributions": [[30, "module-gpflow.probability_distributions"]], "shape (gpflow.probability_distributions.diagonalgaussian property)": [[30, "gpflow.probability_distributions.DiagonalGaussian.shape"]], "shape (gpflow.probability_distributions.gaussian property)": [[30, "gpflow.probability_distributions.Gaussian.shape"]], "shape (gpflow.probability_distributions.markovgaussian property)": [[30, "gpflow.probability_distributions.MarkovGaussian.shape"]], "shape (gpflow.probability_distributions.probabilitydistribution property)": [[30, "gpflow.probability_distributions.ProbabilityDistribution.shape"]], "gh_points_and_weights() (in module gpflow.quadrature.gauss_hermite)": [[31, "gpflow.quadrature.gauss_hermite.gh_points_and_weights"]], "gpflow.quadrature.gauss_hermite": [[31, "module-gpflow.quadrature.gauss_hermite"]], "list_to_flat_grid() (in module gpflow.quadrature.gauss_hermite)": [[31, "gpflow.quadrature.gauss_hermite.list_to_flat_grid"]], "ndgh_points_and_weights() (in module gpflow.quadrature.gauss_hermite)": [[31, "gpflow.quadrature.gauss_hermite.ndgh_points_and_weights"]], "repeat_as_list() (in module gpflow.quadrature.gauss_hermite)": [[31, "gpflow.quadrature.gauss_hermite.repeat_as_list"]], "reshape_z_dz() (in module gpflow.quadrature.gauss_hermite)": [[31, "gpflow.quadrature.gauss_hermite.reshape_Z_dZ"]], "gaussianquadrature (class in gpflow.quadrature)": [[32, "gpflow.quadrature.GaussianQuadrature"]], "ndiagghquadrature (class in gpflow.quadrature)": [[32, "gpflow.quadrature.NDiagGHQuadrature"]], "gpflow.quadrature": [[32, "module-gpflow.quadrature"]], "hermgauss() (in module gpflow.quadrature)": [[32, "gpflow.quadrature.hermgauss"]], "logspace() (gpflow.quadrature.gaussianquadrature method)": [[32, "gpflow.quadrature.GaussianQuadrature.logspace"]], "mvhermgauss() (in module gpflow.quadrature)": [[32, "gpflow.quadrature.mvhermgauss"]], "mvnquad() (in module gpflow.quadrature)": [[32, "gpflow.quadrature.mvnquad"]], "ndiag_mc() (in module gpflow.quadrature)": [[32, "gpflow.quadrature.ndiag_mc"]], "ndiagquad() (in module gpflow.quadrature)": [[32, "gpflow.quadrature.ndiagquad"]], "compute_mypy_flags() (in module gpflow.type_flags)": [[33, "gpflow.type_flags.compute_mypy_flags"]], "gpflow.type_flags": [[33, "module-gpflow.type_flags"]], "dispatcher (class in gpflow.utilities)": [[34, "gpflow.utilities.Dispatcher"]], "add_likelihood_noise_cov() (in module gpflow.utilities)": [[34, "gpflow.utilities.add_likelihood_noise_cov"]], "add_noise_cov() (in module gpflow.utilities)": [[34, "gpflow.utilities.add_noise_cov"]], "assert_params_false() (in module gpflow.utilities)": [[34, "gpflow.utilities.assert_params_false"]], "deepcopy() (in module gpflow.utilities)": [[34, "gpflow.utilities.deepcopy"]], "dispatch() (gpflow.utilities.dispatcher method)": [[34, "gpflow.utilities.Dispatcher.dispatch"]], "dispatch_or_raise() (gpflow.utilities.dispatcher method)": [[34, "gpflow.utilities.Dispatcher.dispatch_or_raise"]], "freeze() (in module gpflow.utilities)": [[34, "gpflow.utilities.freeze"]], "get_first_occurrence() (gpflow.utilities.dispatcher method)": [[34, "gpflow.utilities.Dispatcher.get_first_occurrence"]], "gpflow.utilities": [[34, "module-gpflow.utilities"]], "is_variable() (in module gpflow.utilities)": [[34, "gpflow.utilities.is_variable"]], "leaf_components() (in module gpflow.utilities)": [[34, "gpflow.utilities.leaf_components"]], "multiple_assign() (in module gpflow.utilities)": [[34, "gpflow.utilities.multiple_assign"]], "parameter_dict() (in module gpflow.utilities)": [[34, "gpflow.utilities.parameter_dict"]], "positive() (in module gpflow.utilities)": [[34, "gpflow.utilities.positive"]], "print_summary() (in module gpflow.utilities)": [[34, "gpflow.utilities.print_summary"]], "read_values() (in module gpflow.utilities)": [[34, "gpflow.utilities.read_values"]], "register() (gpflow.utilities.dispatcher method)": [[34, "gpflow.utilities.Dispatcher.register"]], "reset_cache_bijectors() (in module gpflow.utilities)": [[34, "gpflow.utilities.reset_cache_bijectors"]], "select_dict_parameters_with_prior() (in module gpflow.utilities)": [[34, "gpflow.utilities.select_dict_parameters_with_prior"]], "tabulate_module_summary() (in module gpflow.utilities)": [[34, "gpflow.utilities.tabulate_module_summary"]], "to_default_float() (in module gpflow.utilities)": [[34, "gpflow.utilities.to_default_float"]], "to_default_int() (in module gpflow.utilities)": [[34, "gpflow.utilities.to_default_int"]], "training_loop() (in module gpflow.utilities)": [[34, "gpflow.utilities.training_loop"]], "traverse_module() (in module gpflow.utilities)": [[34, "gpflow.utilities.traverse_module"]], "triangular() (in module gpflow.utilities)": [[34, "gpflow.utilities.triangular"]], "triangular_size() (in module gpflow.utilities)": [[34, "gpflow.utilities.triangular_size"]], "broadcasting_elementwise() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.broadcasting_elementwise"]], "difference_matrix() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.difference_matrix"]], "eye() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.eye"]], "gpflow.utilities.ops": [[35, "module-gpflow.utilities.ops"]], "leading_transpose() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.leading_transpose"]], "pca_reduce() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.pca_reduce"]], "square_distance() (in module gpflow.utilities.ops)": [[35, "gpflow.utilities.ops.square_distance"]], "evaluate_parameter_or_function() (in module gpflow.utilities.parameter_or_function)": [[36, "gpflow.utilities.parameter_or_function.evaluate_parameter_or_function"]], "gpflow.utilities.parameter_or_function": [[36, "module-gpflow.utilities.parameter_or_function"]], "prepare_parameter_or_function() (in module gpflow.utilities.parameter_or_function)": [[36, "gpflow.utilities.parameter_or_function.prepare_parameter_or_function"]]}})