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- Description:
BayesPy provides tools for variational Bayesian inference in Python. The model is constructed as a Bayesian network. The aim is to provide a tool which is efficient, flexible and extendable enough for expert use but also accessible for more casual users.
Documentation: http://bayespy.org
Repository: https://github.com/bayespy/bayespy
- Changes to previous version:
- Define extra dependencies needed to build the documentation
- BibTeX Entry: Download
- Supported Operating Systems: Platform Independent
- Data Formats: Hdf, Csv
- Tags: Variational Inference, Bayesian Inference
- Archive: download here
Other available revisons
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Version Changelog Date 0.4.1 - Define extra dependencies needed to build the documentation
November 2, 2015, 13:40:09 0.4.0 - Fix Gaussian node sampling
- Implement Add node for Gaussian nodes
- Raise error if attempting to install on Python 2
- Return both relative and absolute errors from numerical gradient checking
- Add nose plugin to filter unit test warnings appropriately
November 2, 2015, 13:02:37 0.3.7 - Enable keyword arguments when plotting via the inference engine
- Add initial support for logging
September 23, 2015, 14:29:20 0.3.6 - Add maximum likelihood node for the shape parameter of Gamma
- Fix Hinton diagrams for 1-D and 0-D Gaussians
- Fix autosave interval counter
- Fix bugs in constant nodes
September 23, 2015, 13:13:45 0.3.5 - Fix indexing bug in VB optimization (not VB-EM)
- Fix demos
June 9, 2015, 13:17:00 0.3.4 - Fix computation of probability density of Dirichlet nodes
- Use unit tests for all code snippets in docstrings and documentation
June 9, 2015, 12:54:04 0.3.3 - Change license to the MIT license
- Improve SumMultiply efficiency
- Hinton diagrams for gamma variables
- Possible to load only nodes from HDF5 results
June 5, 2015, 16:01:22 0.3.2 - Concatenate node added
- Unit tests for plotting fixed
March 16, 2015, 11:58:37 0.3.1 - Gaussian mixture 2D plotting improvements
- Covariance matrix sampling improvements
- Minor documentation fixes
March 12, 2015, 14:32:34 0.3 - Add gradient-based optimization methods (Riemannian/natural gradient or normal)
- Add collapsed inference
- Add the pattern search method
- Add deterministic annealing
- Add stochastic variational inference
- Add optional input signals to Gaussian Markov chains
- Add unit tests for plotting functions (by Hannu Hartikainen)
- Add printing support to nodes
- Drop Python 3.2 support
March 5, 2015, 09:26:26 0.2.3 - Fix matplotlib compatibility broken by recent changes in matplotlib>=1.4.0
- Add random sampling for Binomial and Bernoulli nodes
- Fix minor bugs, for instance, in plot module
December 3, 2014, 14:51:10 0.2.2 - Fix normalization of categorical Markov chain probabilities (fixes HMM demo)
- Fix initialization from parameter values
November 1, 2014, 11:06:01 0.2.1 Add workaround for matplotlib 1.4.0 bug related to interactive mode which affected monitoring
Fix bugs in Hinton diagrams for Gaussian variables
September 30, 2014, 16:35:11 0.2 added all common distributions: Poisson, beta, multinomial, Bernoulli, categorical, etc
added Gaussian arrays (not just scalars or vectors)
added Gaussian Markov chains with time-varying or swithing dynamics
added discrete Markov chains (enabling hidden Markov models)
added deterministic gating node
added deterministic general sum-product node
added parameter expansion
added new plotting functions: pdf, Hinton diagram
added monitoring of posterior distributions during iteration
improved documentation
August 14, 2014, 17:24:22 0.1 Initial Announcement on mloss.org.
September 25, 2013, 16:10:58
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