Formalism

This page contains the formal presentation of most of the learning algorithms and models implemented in Jajapy. Most of the following describe the algorithms for transition-labelled models, while Jajapy uses state-labelled one. Nevertheless, the algorithms can simply be adapted from one formalism to another.

• HMM

  • Baum-Welch for HMMs pdf

  • A Tutorial on HMMs and Selected Applications in Speech Recognition (L.R. Rabiner) pdf

• MC

  • Baum-Welch for MCs pdf

  • Learning Stochastic Regular Grammars by Means of STate Merging Method (R. Carrasco and J. Oncina) pdf

• MDP

  • Baum-Welch for MDPs pdf

  • Active Learning of Markov Decision Processes using Baum-Welch algorithm (G. Bacci et al.) pdf

  • Learning Deterministic Probabilistic Automata from a Model Checking Perspective (H. Mao et al.) pdf

• CTMC/PCTMC

  • MM Algorithms to Estimate Parameters in Continuous-time Markov Chains (G. Bacci et al.) pdf

• GoHMM

  • Baum-Welch for GoHMMs pdf