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https://github.com/GPflow/GPflow
27 November 2021, 02:00:58 UTC
Revision 4aef40d869462e0aae0920354df6b08a177a54df authored by st-- on 14 October 2019, 21:36:48 UTC, committed by GitHub on 14 October 2019, 21:36:48 UTC 
fix bug in log_prior when a Parameter has a transform but no prior (#1099)
 
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.
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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)
Tip revision: 4aef40d
.coveragerc 
[report]
omit = *tests*, setup.py
exclude_lines =
    pragma: no cover
    def __repr__
    def __str__
    def _repr_html_
    def _html_table_rows
    if self.debug:
    if settings.DEBUG
    raise AssertionError
    raise NotImplementedError
    if __name__ == .__main__.:
    print
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