Inference and Learning in Stochastic Automata

Abstract

Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic automata are a class of input/output devices which can model components in machine learning scenarios. In this paper, we provide an inference algorithm for stochastic automata which is related to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and describe a more efficient implementation which is related to the Baum-Welch algorithm.

Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing Units

“…An automaton which change their state and give output according to probability is called stochastic automata. Stochastic automata has been introduced as systems which change their state and give some output according to some probability depending on the input and the actual state [11].…”

“…n , the Forward algorithm finds the sequence of states that correspond to this input and output behavior [11]. This algorithm initializes the forward matrix F with fraction 1 m.m 1 .…”

“…The computational time complexity of Forward algorithm is O(m 2 n). The optimal state sequenceq can be computed efficiently using tropicalization of sum-product decomposition [11]. For this, put…”

“…The sums and products are replaced by tropical addition and tropical multiplication in sum-product decomposition [11].…”

“…In stochastic automata, the inference algorithm called Forward algorithm is a variant of Viterbi algorithm [11]. Forward algorithm is a dynamic programming algorithm to find the optimal sequence of states.…”