New features
- Add documentation section on survival analysis and censored data models
- Add
check_test_point
method topm.Model
- Add
Ordered
Transformation andOrderedLogistic
distribution - Add
Chain
transformation - Improve error message
Mass matrix contains zeros on the diagonal. Some derivatives might always be zero
during tuning ofpm.sample
- Improve error message
NaN occurred in optimization.
during ADVI - Save and load traces without
pickle
usingpm.save_trace
andpm.load_trace
- Add
Kumaraswamy
distribution - Add
TruncatedNormal
distribution - Rewrite parallel sampling of multiple chains on py3. This resolves
long standing issues when transferring large traces to the main process,
avoids pickling issues on UNIX, and allows us to show a progress bar
for all chains. If parallel sampling is interrupted, we now return
partial results. - Add
sample_prior_predictive
which allows for efficient sampling from
the unconditioned model. - SMC: remove experimental warning, allow sampling using
sample
, reduce autocorrelation from
final trace. - Add
model_to_graphviz
(which uses the optional dependencygraphviz
) to
plot a directed graph of a PyMC3 model using plate notation. - Add beta-ELBO variational inference as in beta-VAE model (Christopher P. Burgess et al. NIPS, 2017)
- Add
__dir__
toSingleGroupApproximation
to improve autocompletion in interactive environments
Fixes
- Fixed grammar in divergence warning, previously
There were 1 divergences ...
could be raised. - Fixed
KeyError
raised when only subset of variables are specified to be recorded in the trace. - Removed unused
repeat=None
arguments from allrandom()
methods in distributions. - Deprecated the
sigma
argument inMarginalSparse.marginal_likelihood
in favor ofnoise
- Fixed unexpected behavior in
random
. Now therandom
functionality is more robust and will work better forsample_prior
when that is implemented. - Fixed
scale_cost_to_minibatch
behaviour, previously this was not working and alwaysFalse