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• W2950601539 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 alpha-stable distribution, g-and-k distribution, and the stochastic volatility model with alpha-stable returns, using both real and synthetic data." @default.
• W2950601539 created "2019-06-27" @default.
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• W2950601539 date "2013-11-17" @default.
• W2950601539 modified "2023-09-27" @default.
• W2950601539 title "Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo" @default.
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