./math/py-pymc3, Bayesian modeling and probabilistic machine learning

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Branch: CURRENT, Version: 3.4.1, Package name: py27-pymc3-3.4.1, Maintainer: minskim

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.


Required to run:
[graphics/py-matplotlib] [devel/py-setuptools] [math/py-numpy] [lang/python27] [math/py-pandas] [lang/py-six] [devel/py-h5py] [devel/py-enum34] [math/py-patsy] [misc/py-tqdm] [math/py-Theano]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 859916e93bf653a15bf0637e63067714d17834ce
RMD160: 9fed35a862543f2dcaa064efae0b57f7e334427f
Filesize: 46424.527 KB

Version history: (Expand)


CVS history: (Expand)


   2018-07-23 03:37:54 by Min Sik Kim | Files touched by this commit (3) | Package updated
Log message:
math/py-pymc3: 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 beta-ELBO variational inference as in beta-VAE model
  (Christopher P. Burgess et al. NIPS, 2017)
- Add __dir__ to SingleGroupApproximation to improve autocompletion in
  interactive environments
   2018-07-06 05:46:44 by Min Sik Kim | Files touched by this commit (4)
Log message:
math/py-pymc3: 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.