https://github.com/GPflow/GPflow
Revision 4aef40d869462e0aae0920354df6b08a177a54df authored by st-- on 14 October 2019, 21:36:48 UTC, committed by GitHub on 14 October 2019, 21:36:48 UTC
Fixes a bug in the optimization objective for all `BayesianModel` subclasses when the model has parameters that have a `transform` (e.g. `positive()`, for kernel lengthscales and variances) but no `prior` (i.e. when doing Maximum Likelihood Estimation (MLE) inference). The objective should be independent of the parametrisation of the parameters, i.e. `log_prior()` of the parameters should be zero.
1 parent c5b91dd
Tip revision: 4aef40d869462e0aae0920354df6b08a177a54df authored by st-- on 14 October 2019, 21:36:48 UTC
fix bug in log_prior when a Parameter has a transform but no prior (#1099)
fix bug in log_prior when a Parameter has a transform but no prior (#1099)
Tip revision: 4aef40d
File | Mode | Size |
---|---|---|
conditionals | ||
config | ||
covariances | ||
expectations | ||
inducing_variables | ||
kernels | ||
likelihoods | ||
models | ||
optimizers | ||
utilities | ||
__init__.py | -rw-r--r-- | 960 bytes |
base.py | -rw-r--r-- | 6.7 KB |
ci_utils.py | -rw-r--r-- | 964 bytes |
kullback_leiblers.py | -rw-r--r-- | 4.7 KB |
logdensities.py | -rw-r--r-- | 3.0 KB |
mean_functions.py | -rw-r--r-- | 5.8 KB |
probability_distributions.py | -rw-r--r-- | 1.7 KB |
quadrature.py | -rw-r--r-- | 7.7 KB |
versions.py | -rw-r--r-- | 177 bytes |
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