There was no 3.4 release due to a naming issue on PyPI.

New features

• Add logit_p keyword to pm.Bernoulli, so that users can specify the logit of the success probability. This is faster and more stable than using p=tt.nnet.sigmoid(logit_p).
• Add random keyword to pm.DensityDist thus enabling users to pass custom random method which in turn makes sampling from a DensityDist possible.
• Effective sample size computation is updated. The estimation uses Geyer's initial positive sequence, which no longer truncates the autocorrelation series inaccurately. pm.diagnostics.effective_n now can reports N_eff>N.
• Added KroneckerNormal distribution and a corresponding MarginalKron
  Gaussian Process implementation for efficient inference, along with
  lower-level functions such as cartesian and kronecker products.
• Added Coregion covariance function.
• Add new 'pairplot' function, for plotting scatter or hexbin matrices of sampled parameters.
  Optionally it can plot divergences.
• Plots of discrete distributions in the docstrings
• Add logitnormal distribution
• Densityplot: add support for discrete variables
• Fix the Binomial likelihood in .glm.families.Binomial, with the flexibility of specifying the n.
• Add offset kwarg to .glm.
• Changed the compare function to accept a dictionary of model-trace pairs instead of two separate lists of models and traces.
• add test and support for creating multivariate mixture and mixture of mixtures
• distribution.draw_values, now is also able to draw values from conditionally dependent RVs, such as autotransformed RVs (Refer to PR #2902).

Fixes

• VonMises does not overflow for large values of kappa. i0 and i1 have been removed and we now use log_i0 to compute the logp.
• The bandwidth for KDE plots is computed using a modified version of Scott's rule. The new version uses entropy instead of standard deviation. This works better for multimodal distributions. Functions using KDE plots has a new argument bw controlling the bandwidth.
• fix PyMC3 variable is not replaced if provided in more_replacements (#2890)
• Fix for issue #2900. For many situations, named node-inputs do not have a random method, while some intermediate node may have it. This meant that if the named node-input at the leaf of the graph did not have a fixed value, theano would try to compile it and fail to find inputs, raising a theano.gof.fg.MissingInputError. This was fixed by going through the theano variable's owner inputs graph, trying to get intermediate named-nodes values if the leafs had failed.
• In distribution.draw_values, some named nodes could be theano.tensor.TensorConstants or theano.tensor.sharedvar.SharedVariables. Nevertheless, in distribution._draw_value, these would be passed to distribution._compile_theano_function as if they were theano.tensor.TensorVariables. This could lead to the following exceptions TypeError: ('Constants not allowed in param list', ...) or TypeError: Cannot use a shared variable (...). The fix was to not add theano.tensor.TensorConstant or theano.tensor.sharedvar.SharedVariable named nodes into the givens dict that could be used in distribution._compile_theano_function.
• Exponential support changed to include zero values.

Deprecations

• DIC and BPIC calculations have been removed
• df_summary have been removed, use summary instead
• njobs and nchains kwarg are deprecated in favor of cores and chains for sample
• lag kwarg in pm.stats.autocorr and pm.stats.autocov is deprecated.