pymc-devs/pymc v3.3 on GitHub

New features

Improve NUTS initialization advi+adapt_diag_grad and add jitter+adapt_diag_grad (#2643)
Added MatrixNormal class for representing vectors of multivariate normal variables
Implemented HalfStudentT distribution
New benchmark suite added (see http://pandas.pydata.org/speed/pymc3/)
Generalized random seed types
Update loo, new improved algorithm (#2730)
New CSG (Constant Stochastic Gradient) approximate posterior sampling algorithm (#2544)
Michael Osthege added support for population-samplers and implemented differential evolution metropolis (DEMetropolis). For models with correlated dimensions that can not use gradient-based samplers, the DEMetropolis sampler can give higher effective sampling rates. (also see PR#2735)
Forestplot supports multiple traces (#2736)
Add new plot, densityplot (#2741)
DIC and BPIC calculations have been deprecated
Refactor HMC and implemented new warning system (#2677, #2808)

Fixed compareplot to use loo output.
Improved posteriorplot to scale fonts
sample_ppc_w now broadcasts
df_summary function renamed to summary
Add test for model.logp_array and model.bijection (#2724)
Fixed sample_ppc and sample_ppc_w to iterate all chains(#2633, #2748)
Add Bayesian R2 score (for GLMs) stats.r2_score (#2696) and test (#2729).
SMC works with transformed variables (#2755)
Speedup OPVI (#2759)
Multiple minor fixes and improvements in the docs (#2775, #2786, #2787, #2789, #2790, #2794, #2799, #2809)