Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models

2012 ◽  
Vol 44 ◽  
pp. 70-77 ◽  
Author(s):  
Meng Gao ◽  
Hui Zhang
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
State Space ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Sequential Monte Carlo Methods ◽  
Nonlinear State Space Models ◽  
Nonlinear State

scholarly journals Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models

Bernoulli ◽  
2008 ◽  
Vol 14 (1) ◽  
pp. 155-179 ◽  
Author(s):  
Jimmy Olsson ◽  
Olivier Cappé ◽  
Randal Douc ◽  
Éric Moulines
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
State Space ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Nonlinear State Space Models ◽  
Nonlinear State

scholarly journals MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

2019 ◽  
Vol 33 ◽  
pp. 7858-7865 ◽  
Author(s):  
Mahdi Imani ◽  
Seyede Fatemeh Ghoreishi ◽  
Douglas Allaire ◽  
Ulisses M. Braga-Neto
Keyword(s):  
State Space ◽  
Parameter Space ◽  
Sequential Monte Carlo ◽  
Optimization Techniques ◽  
State Space Models ◽  
Bayesian Optimization ◽  
Particle Methods ◽  
Estimation Methods ◽  
Nonlinear State Space Models ◽  
Nonlinear State

Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample-based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.

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