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math/pypymc3,
Bayesian modeling and probabilistic machine learning
Branch: CURRENT,
Version: 3.5,
Package name: py27pymc33.5,
Maintainer: minskimPyMC3 is a Python package for Bayesian statistical modeling and
Probabilistic Machine Learning focusing on advanced Markov chain Monte
Carlo (MCMC) and variational inference (VI) algorithms. Its
flexibility and extensibility make it applicable to a large suite of
problems.
Required to run:[
graphics/pymatplotlib] [
devel/pysetuptools] [
math/pynumpy] [
lang/python27] [
math/pypandas] [
lang/pysix] [
devel/pyh5py] [
devel/pyenum34] [
math/pypatsy] [
misc/pytqdm] [
math/pyTheano] [
devel/pyjoblib]
Required to build:[
pkgtools/cwrappers]
Master sites:
SHA1: d5a699bafd327e455e96fc813915807c1cc86325
RMD160: 8fd3b10bf359a4462e1c7397e69b507e706f94b3
Filesize: 31246.871 KB
Version history: (Expand)
 (20180724) Updated to version: py27pymc33.5
 (20180706) Package added to pkgsrc.se, version py27pymc33.4.1 (created)
CVS history: (Expand)
20180723 03:37:54 by Min Sik Kim  Files touched by this commit (3)  
Log message:
math/pypymc3: Update to 3.5
New features:
 Add documentation section on survival analysis and censored data
models
 Add check_test_point method to pm.Model
 Add Ordered Transformation and OrderedLogistic distribution
 Add Chain transformation
 Improve error message Mass matrix contains zeros on the
diagonal. Some derivatives might always be zero during tuning of
pm.sample
 Improve error message NaN occurred in optimization. during ADVI
 Save and load traces without pickle using pm.save_trace and
pm.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 dependency graphviz)
to plot a directed graph of a PyMC3 model using plate notation.
 Add betaELBO variational inference as in betaVAE model
(Christopher P. Burgess et al. NIPS, 2017)
 Add __dir__ to SingleGroupApproximation to improve autocompletion in
interactive environments

20180706 05:46:44 by Min Sik Kim  Files touched by this commit (4) 
Log message:
math/pypymc3: Import version 3.4.1
PyMC3 is a Python package for Bayesian statistical modeling and
Probabilistic Machine Learning focusing on advanced Markov chain Monte
Carlo (MCMC) and variational inference (VI) algorithms. Its
flexibility and extensibility make it applicable to a large suite of
problems.
