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SAutomata  

Inference and Learning in Stochastic Automata
View on CRAN: Click here


Download and install SAutomata package within the R console
Install from CRAN:
install.packages("SAutomata")

Install from Github:
library("remotes")
install_github("cran/SAutomata")

Install by package version:
library("remotes")
install_version("SAutomata", "0.1.0")



Attach the package and use:
library("SAutomata")
Maintained by
Muhammad Kashif Hanif
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-11-02
Latest Update: 2018-11-02
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) .
How to cite:
Muhammad Kashif Hanif (2018). SAutomata: Inference and Learning in Stochastic Automata. R package version 0.1.0, https://cran.r-project.org/web/packages/SAutomata. Accessed 07 Nov. 2024.
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Complete documentation for SAutomata
Functions, R codes and Examples using the SAutomata R package
Some associated functions: BaumWelch . Sbackward . Sforward . TOC.sampleData . initSA . scores . 
Some associated R codes: BaumWelch.R . Sbackward.R . Sfoward.R . TOC.R . initSA.R . scores.R .  Full SAutomata package functions and examples
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