Title :
Nested Monte Carlo EM algorithm for switching state-space models
Author :
Popescu, Cristina Adela ; Wong, Yau Shu
Author_Institution :
Grant MacEwan Coll., Edmonton, Alta., Canada
Abstract :
Switching state-space models have been widely used in many applications arising from science, engineering, economic, and medical research. In this paper, we present a Monte Carlo Expectation Maximization (MCEM) algorithm for learning the parameters and classifying the states of a state-space model with a Markov switching. A stochastic implementation based on the Gibbs sampler is introduced in the expectation step of the MCEM algorithm. We study the asymptotic properties of the proposed algorithm, and we also describe how a nesting approach and the Rao-Blackwellized forms can be employed to accelerate the rate of convergence of the MCEM algorithm. Finally, the performance and the effectiveness of the proposed method are demonstrated by applications to simulated and physiological experimental data.
Keywords :
Markov processes; Monte Carlo methods; data mining; learning (artificial intelligence); optimisation; pattern classification; state-space methods; temporal databases; Gibbs sampler; Markov switching; Monte Carlo Expectation Maximization algorithm; Rao-Blackwellized form; asymptotic algorithm properties; physiological experimental data; state-space models; Acceleration; Biomedical engineering; Brain modeling; Convergence; Econometrics; Machine learning; Machine learning algorithms; Medical diagnostic imaging; Monte Carlo methods; Stochastic processes; Index Terms- Time series analysis; Kalman filtering; Markov processes; Monte Carlo simulation.; machine learning; parameter learning; probabilistic algorithms;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2005.202
