# Computations in Stochastic Acceptors

## TL;DR

This paper introduces dynamic programming algorithms for stochastic acceptors, enabling computation of input marginals, acceptance probabilities, and parameter estimation using EM and Baum-Welch algorithms.

## Contribution

It provides novel algorithms for probabilistic automata, including efficient parameter estimation methods, advancing their application in machine learning contexts.

## Key findings

- Algorithms for input marginal computation
- Acceptance probability calculation methods
- Efficient EM-based parameter estimation

## Abstract

Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic acceptors or probabilistic automata are stochastic automata without output that can model components in machine learning scenarios. In this paper, we provide dynamic programming algorithms for the computation of input marginals and the acceptance probabilities in stochastic acceptors. Furthermore, we specify an algorithm for the parameter estimation of the conditional probabilities using the expectation-maximization technique and a more efficient implementation related to the Baum-Welch algorithm.