A Sequential Monte Carlo framework for the system identification of jump Markov state space models

2014 ◽  
Author(s):  
Trevor T. Ashley ◽  
Sean B. Andersson
Keyword(s):  
Monte Carlo ◽  
System Identification ◽  
State Space ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Markov State ◽  
Monte Carlo Framework

Sequential Monte Carlo Optimization Using Artificial State-Space Models

2009 ◽  
Author(s):  
Joaquin Miguez ◽  
Cristina S. Maiz ◽  
Petar M. Djuric ◽  
Dan Crisan
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Monte Carlo Optimization

scholarly journals A closed-form filter for binary time series

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Augusto Fasano ◽  
Giovanni Rebaudo ◽  
Daniele Durante ◽  
Sonia Petrone
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Closed Form ◽  
State Space ◽  
Binary Data ◽  
Sequential Monte Carlo ◽  
State Space Models ◽  
Cumulative Distribution ◽  
Gaussian State ◽  
Binary Time Series

AbstractNon-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models — where filtering, predictive and smoothing distributions are available in closed form — binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the Gaussian states and the likelihood induced by the observation equation for the binary data. In this article we prove that the filtering, predictive and smoothing distributions in dynamic probit models with Gaussian state variables are, in fact, available and belong to a class of unified skew-normals (sun) whose parameters can be updated recursively in time via analytical expressions. Also the key functionals of these distributions are, in principle, available, but their calculation requires the evaluation of multivariate Gaussian cumulative distribution functions. Leveraging sun properties, we address this issue via novel Monte Carlo methods based on independent samples from the smoothing distribution, that can easily be adapted to the filtering and predictive case, thus improving state-of-the-art approximate and sequential Monte Carlo inference in small-to-moderate dimensional studies. Novel sequential Monte Carlo procedures that exploit the sun properties are also developed to deal with online inference in high dimensions. Performance gains over competitors are outlined in a financial application.

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