1.2.5

• New feature: Model.filter to express constrained models
• New feature: Trace.variable_sizes gives a list of variables in a trace, sorted by their memory usage
• New feature: Empirical.reweight allows recomputing the weights of a weighted Empirical
• New feature: Empirical.reobserve allows modifying the likelihood distributions of an already sampled weighted posterior distribution (from an importance-sampling-based inference engine), so that likelihood terms can be tuned/calibrated without re-running the model prior. Idea by Giacomo Acciarini.
• Raise an error if the observed value is None in posterior conditioning
• Print effective sample size on-the-fly while sampling posteriors with importance-sampling-based inference engines
• Model.sample returns a Trace object sampled from the model prior (equivalent to Model.get_trace which will be deprecated)
• Added Bernoulli support for inference compilation
• Removed the replace feature from the pyprob.sample API
• Exclude tagged variables from diagnostics
• Inference network layers are not pre-generated by default when training with OfflineDataset
• Added support for moving distribution, variables, traces between compute devices
• OfflineDataset.save_sorted supports moving dataset between devices