SAutomata: Inference and Learning in Stochastic Automata
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) <doi:10.12732/ijpam.v115i3.15>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.0.0) |
| Published: |
2018-11-02 |
| Author: |
Muhammad Kashif Hanif [cre, aut],
Muhammad Umer Sarwar [aut],
Rehman Ahmad [aut],
Zeeshan Ahmad [aut],
Karl-Heinz Zimmermann [aut] |
| Maintainer: |
Muhammad Kashif Hanif <mkashifhanif at gcuf.edu.pk> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
SAutomata results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=SAutomata
to link to this page.