scholarly journals Transdimensional sequential Monte Carlo for hidden Markov models using variational Bayes - SMCVB

2014 ◽  
Author(s):  
Clare McGrory ◽  
Daniel Ahfock
Keyword(s):  
Monte Carlo ◽  
Hidden Markov Models ◽  
Sequential Monte Carlo ◽  
Markov Models ◽  
Hidden Markov ◽  
Variational Bayes

scholarly journals On the Behaviour of the Backward Interpretation of Feynman-Kac Formulae Under Verifiable Conditions

2015 ◽  
Vol 52 (02) ◽  
pp. 339-359 ◽  
Author(s):  
Ajay Jasra
Keyword(s):  
Monte Carlo ◽  
Hidden Markov Models ◽  
Sequential Monte Carlo ◽  
Markov Models ◽  
Hidden Markov ◽  
Asymptotic Variance ◽  
Time Behaviour ◽  
Additive Functionals ◽  
Monte Carlo Estimate ◽  
Carlo Estimate

We consider the time behaviour associated to the sequential Monte Carlo estimate of the backward interpretation of Feynman-Kac formulae. This is particularly of interest in the context of performing smoothing for hidden Markov models. We prove a central limit theorem under weaker assumptions than adopted in the literature. We then show that the associated asymptotic variance expression for additive functionals grows at most linearly in time under hypotheses that are weaker than those currently existing in the literature. The assumptions are verified for some hidden Markov models.

scholarly journals Sequential Monte Carlo smoothing for general state space hidden Markov models

2011 ◽  
Vol 21 (6) ◽  
pp. 2109-2145 ◽  
Author(s):  
Randal Douc ◽  
Aurélien Garivier ◽  
Eric Moulines ◽  
Jimmy Olsson
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Hidden Markov Models ◽  
Sequential Monte Carlo ◽  
Markov Models ◽  
Hidden Markov ◽  
General State ◽  
General State Space

scholarly journals On the Behaviour of the Backward Interpretation of Feynman-Kac Formulae Under Verifiable Conditions

2015 ◽  
Vol 52 (2) ◽  
pp. 339-359 ◽  
Author(s):  
Ajay Jasra
Keyword(s):  
Monte Carlo ◽  
Hidden Markov Models ◽  
Sequential Monte Carlo ◽  
Markov Models ◽  
Hidden Markov ◽  
Asymptotic Variance ◽  
Time Behaviour ◽  
Additive Functionals ◽  
Monte Carlo Estimate ◽  
Carlo Estimate

We consider the time behaviour associated to the sequential Monte Carlo estimate of the backward interpretation of Feynman-Kac formulae. This is particularly of interest in the context of performing smoothing for hidden Markov models. We prove a central limit theorem under weaker assumptions than adopted in the literature. We then show that the associated asymptotic variance expression for additive functionals grows at most linearly in time under hypotheses that are weaker than those currently existing in the literature. The assumptions are verified for some hidden Markov models.

scholarly journals On some Extensions of the Sequential Monte Carlo methods in high-order Hidden Markov Models

2019 ◽  
Vol 14 (2) ◽  
pp. 1977-1998
Author(s):  
Mouhamad Mounirou Allaya ◽  
Alioune Coulibaly ◽  
El Hadj Dème ◽  
Mouhamadou Moustapha Kâ ◽  
Babacar Sène
Keyword(s):  
Monte Carlo ◽  
Hidden Markov Models ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Markov Models ◽  
Hidden Markov ◽  
High Order ◽  
Sequential Monte Carlo Methods
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