pymc-devs/pymc v3.6

on GitHub

This is a major new release from 3.5 with many new features and important bugfixes. The highlight is certainly our completely revamped website: https://docs.pymc.io/

Note also, that this release will be the last to be compatible with Python 2. Thanks to all contributors!

New features

• Track the model log-likelihood as a sampler stat for NUTS and HMC samplers
  (accessible as trace.get_sampler_stats('model_logp')) (#3134)
• Add Incomplete Beta function incomplete_beta(a, b, value)
• Add log CDF functions to continuous distributions: Beta, Cauchy, ExGaussian, Exponential, Flat, Gumbel, HalfCauchy, HalfFlat, HalfNormal, Laplace, Logistic, Lognormal, Normal, Pareto, StudentT, Triangular, Uniform, Wald, Weibull.
• Behavior of sample_posterior_predictive is now to produce posterior predictive samples, in order, from all values of the trace. Previously, by default it would produce 1 chain worth of samples, using a random selection from the trace (#3212)
• Show diagnostics for initial energy errors in HMC and NUTS.
• PR #3273 has added the distributions.distribution._DrawValuesContext context
  manager. This is used to store the values already drawn in nested random
  and draw_values calls, enabling draw_values to draw samples from the
  joint probability distribution of RVs and not the marginals. Custom
  distributions that must call draw_values several times in their random
  method, or that invoke many calls to other distribution's random methods
  (e.g. mixtures) must do all of these calls under the same _DrawValuesContext
  context manager instance. If they do not, the conditional relations between
  the distribution's parameters could be broken, and random could return
  values drawn from an incorrect distribution.
• Rice distribution is now defined with either the noncentrality parameter or the shape parameter (#3287).

Maintenance

• Big rewrite of documentation (#3275)
• Fixed Triangular distribution c attribute handling in random and updated sample codes for consistency (#3225)
• Refactor SMC and properly compute marginal likelihood (#3124)
• Removed use of deprecated ymin keyword in matplotlib's Axes.set_ylim (#3279)
• Fix for #3210. Now distribution.draw_values(params), will draw the params values from their joint probability distribution and not from combinations of their marginals (Refer to PR #3273).
• Removed dependence on pandas-datareader for retrieving Yahoo Finance data in examples (#3262)
• Rewrote Multinomial._random method to better handle shape broadcasting (#3271)
• Fixed Rice distribution, which inconsistently mixed two parametrizations (#3286).
• Rice distribution now accepts multiple parameters and observations and is usable with NUTS (#3289).
• sample_posterior_predictive no longer calls draw_values to initialize the shape of the ppc trace. This called could lead to ValueError's when sampling the ppc from a model with Flat or HalfFlat prior distributions (Fix issue #3294).

Deprecations

• Renamed sample_ppc() and sample_ppc_w() to sample_posterior_predictive() and sample_posterior_predictive_w(), respectively.