On estimation states of hidden markov models in condition of unknown transition matrix

2015/02/28 by Vasily Vasilyev, Vasilyev, Vasily, А. В. Добровидов +1
Computer Science · Engineering · #Control Systems and Identification #Distributed Sensor Networks and Detection Algorithms #FOS: Mathematics #Fault Detection and Control Systems #Probability (math.PR) #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1503.00167

openalex publication_date 2015/02/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

In this paper, we develop methods of nonlinear filtering and prediction of an unobservable Markov chain with a finite set of states. This Markov chain controls coefficients of AR(p) model. Using observations generated by AR(p) model we have to estimate the state of Markov chain in the case of an unknown probability transition matrix. Comparison of proposed non-parametric algorithms with the optimal methods in the case of the known transition matrix is carried out by simulating.

Related

• Markov Observation Models
• Nonparametric identification and maximum likelihood estimation for hidden Markov model
• Finite state space non parametric Hidden Markov Models are in general identifiable
• Estimating the transition matrix of a Markov chain observed at random times
• Robust Filtering and Propagation of Uncertainty in Hidden Markov Models