scholarly journals Sequential Monte Carlo for inference in nonlinear state space models

2014 ◽  
Author(s):  
Johan Dahlin ◽  
Keyword(s):  
Monte Carlo ◽  
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.

Sequential Monte Carlo Methods

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Dsge Models ◽  
Additional Advantage ◽  
Mcmc Algorithms ◽  
Convergence Results ◽  
Posterior Inference ◽  
Nonlinear State Space Models ◽  
Nonlinear State

This chapter analyzes Sequential Monte Carlo (SMC) algorithms and how they were initially developed to solve filtering problems that arise in nonlinear state–space models. The first paper that applied SMC techniques to posterior inference in DSGE models is Creal (2007). Herbst and Schorfheide (2014) developed the algorithm further, provided some convergence results for an adaptive version of the algorithm, and showed that a properly tailored SMC algorithm delivers more reliable posterior inference for largescale DSGE models with multimodal posteriors than the widely used RMWHV algorithm. An additional advantage of the SMC algorithms over MCMC algorithms, on the computational front, highlighted by Durham and Geweke (2014), is that SMC is much more amenable to parallelization.

scholarly journals Identification of the Silverbox Benchmark Using Nonlinear State-Space Models

2012 ◽  
Vol 45 (16) ◽  
pp. 632-637 ◽  
Author(s):  
Anna Marconato ◽  
Jonas Sjöberg ◽  
Johan Suykens ◽  
Johan Schoukens
Keyword(s):  
State Space ◽  
State Space Models ◽  
Nonlinear State Space Models ◽  
Nonlinear State
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