Exact Bayesian estimation in constrained Triplet Markov Chains
Résumé
The Jump Markov state-space system (JMSS) is a well known model for representing dynamical models with jumps. However inference in a JMSS model is NP-hard, even in the conditionally linear and Gaussian case. Suboptimal solutions include Sequential Monte Carlo (SMC) and Interacting Multiple Models (IMM) methods. In this paper, we build a constrained Triplet Markov Chain (TMC) model which is close to the given JMSS model, and in which moments of interest can be computed exactly (without resorting to numerical nor Monte Carlo approximations) and at a computational cost which is linear in the number of observations. Additionally, a side advantage of our technique is that it can be used easily in a partially known model context
Mots clés
Statistical analysis
Signal processing
Exact Bayesian estimation
IMM
SMC
TMC model
Constrained triplet Markov chain model
Sequential Monte Carlo method
Hidden Markov models
Computational modeling
Expectation maximization
Bayesian estimation
Triplet Markov chains
Jump Markov state-space system
Monte Carlo methods
Numerical models
Switches
Approximation methods
Markov processes
Interacting multiple model method
NP-hard problem
Dynamical models
JMSS model
Fichier principal
article_YohanPetetin_Exact Bayesian estimation.pdf (102.67 Ko)
Télécharger le fichier
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...