Mocapy Dynamic Bayesian Network toolkit implemented in Python
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perform toolkit bayesian Bayesian toolkit perform inference parameter learner

Mocapy Description

Dynamic Bayesian Network toolkit implemented in Python Mocapy is a freely available toolkit that performs maximum likelihood (ML) or maximum a posteriori (MAP) parameter learning and inference in Dynamic Bayesian Networks (DBNs), using Markov Chain Monte Carlo (MCMC) methods Mocapy supports discrete, Kent, Von Mises-Fisher Gaussian and Dirichlet nodes.One of the special features of Mocapy is that parameter learning can be done on a cluster computer, which makes the inherently slow MCMC approach applicable for real-life DBN architectures and data set sizes. Requirements: · Python What's New in This Release: · Fixed memory bug (delete -> delete[])

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