  Jump Markov linear models consists of a finite number of linear state space
models and a discrete variable encoding the jumps (or switches) between the
different linear models. Identifying jump Markov linear models makes for a
challenging problem lacking an analytical solution. We derive a new expectation
maximization (EM) type algorithm that produce maximum likelihood estimates of
the model parameters. Our development hinges upon recent progress in combining
particle filters with Markov chain Monte Carlo methods in solving the nonlinear
state smoothing problem inherent in the EM formulation. Key to our development
is that we exploit a conditionally linear Gaussian substructure in the model,
allowing for an efficient algorithm.
