Parallel EM algorithm on Hadoop for large-scale hidden Markov model parameter estimation

Abstract

This paper aims to propose a parallel EM algorithm based on Hadoop for parameter estimation of large-scale hidden Markov models (HMM). HMM is a commonly used statistical model. However, since the parameter estimation of HMM involves the storage and processing of large-scale data sets, traditional serial algorithms have certain limitations in efficiency. This paper introduces the Hadoop parallel computing framework, divides the task into multiple subtasks through the MapReduce programming model and assigns them to different machines for parallel computing, which improves the efficiency and scalability of parameter estimation. The results show that using parallel EM algorithm for large-scale hidden Markov model parameter estimation on Hadoop is feasible and effective.