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

Sinan Yıldırım, Sumeetpal S. Singh, Thomas Dean, Ajay Jasra

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.
Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
Volume24
Issue number3
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

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