CUED Publications database

Parameter Estimation in Hidden Markov Models With Intractable Likelihoods Using Sequential Monte Carlo

Yıldırım, S and Singh, SS and Dean, T and Jasra, A (2015) Parameter Estimation in Hidden Markov Models With Intractable Likelihoods Using Sequential Monte Carlo. Journal of Computational and Graphical Statistics, 24. pp. 846-865. ISSN 1061-8600

Full text not available from this repository.

Abstract

© 2015 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. We propose sequential Monte Carlo-based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation. The static parameter estimation algorithms are gradient-based and cover both offline and online estimation. We demonstrate their performance by estimating the parameters of three intractable models, namely the α-stable distribution, g-and-k distribution, and the stochastic volatility model with α-stable returns, using both real and synthetic data.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:58
Last Modified: 23 Nov 2017 04:23
DOI: