Bayesian Parameter Identification for Jump Markov Linear Systems

2020/04/18 by Mark P. Balenzuela, Balenzuela, Mark P., Adrian Wills +5
Computer Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Methodology (stat.ME) #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2004.08565

openalex publication_date 2020/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertainty without relying on asymptotic in data-length arguments. To achieve this, the paper details a particle-Gibbs sampling approach that provides samples from the desired posterior distribution. These samples are produced by utilising a modified discrete particle filter and carefully chosen conjugate priors.

Citations

• Interacting multiple model methods in target tracking: a survey
• The interacting multiple model algorithm for systems with Markovian switching coefficients
• On Gibbs sampling for state space models
• Monte Carlo sampling methods using Markov chains and their applications

Related

• Efficient parameter sampling for Markov jump processes
• Bayesian inference for Markov jump processes with informative observations
• Recursive maximum likelihood identification of jump Markov nonlinear systems
• Particle Gibbs algorithms for Markov jump processes
• Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices