  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.
