Parameter Estimation in Hidden Markov Models With Intractable Likelihoods Using Sequential Monte Carlo
نویسندگان
چکیده
منابع مشابه
Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
Approximate Bayesian computation (ABC) is a popular technique for approximating likelihoods and is often used in parameter estimation when the likelihood functions are analytically intractable. Although the use of ABC is widespread in many fields, there has been little investigation of the theoretical properties of the resulting estimators. In this paper we give a theoretical analysis of the as...
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We present a learning algorithm for hidden Markov models with continuous state and observation spaces. All necessary probability density functions are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data, just as in regular HMM learning. Regularization during learning is obtained using an...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2015
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2014.938811