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SAutomata (version 0.1.0)

Inference and Learning in Stochastic Automata

Description

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) .

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Version

Install

install.packages('SAutomata')

Monthly Downloads

155

Version

0.1.0

License

GPL (>= 3)

Maintainer

Muhammad Kashif Hanif

Last Published

November 2nd, 2018

Functions in SAutomata (0.1.0)

BaumWelch

Inferring the Forward and Backward Probabilities of a Stochastic Automata Model via the Baum-Welch algorithm
TOC.sampleData

Learning (Not For End User)
initSA

Initialisation of SA's
scores

Calculation of Probabilities (Not For End User)
Sforward

Computes The Forward Probabilities
Sbackward

Computes The Backward Probabilities