The toolbox contains routines for manipulating probabilities and forming structured distributions. The code accompanies a textbook on Bayesian Reasoning and Machine Learning which is freely available from the project website. There is an emphasis on discrete variables, though the software is extendible to continuous distributions as well. Standard methods are supported including Junction Trees, Influence Diagrams, Markov Decision Processes, Factor Graphs, Hidden Markov models, Linear Dynamical Systems, Switching LDSs, PCA with missing data, various matrix factorisation routines and many more.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Agnostic
- Data Formats: Matlab
- Tags: Kalman Filter, Machine Learning, Exact Bayesian Methods, Approximate Inference, Bayesian Networks, Factor Graphs, Junction Tree, Exact Inference, Hidden Markov Model, Gaussian Processes, Markov Networ
- Archive: download here
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