Rapfff/jajapy
Baum-Welch for all kind of Markov models
This library helps researchers and engineers who work with probabilistic systems to automatically build detailed models of how these systems behave. By inputting sequences of observations or interactions, it outputs various types of Markov models like Hidden Markov Models (HMMs) or Markov Decision Processes (MDPs) that accurately represent the underlying system dynamics. This is especially useful for those performing formal verification or performance analysis.
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Use this if you need to learn the parameters and structure of complex stochastic models from observed data, particularly when working with model checking tools like Storm or Prism.
Not ideal if you are looking for a general-purpose machine learning library or if your problem doesn't involve formal verification or probabilistic model analysis.
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23
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2
Language
Python
License
MIT
Category
Last pushed
Apr 07, 2024
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