  Jump Markov linear systems (JMLS) are a useful class which can be used to
model processes which exhibit random changes in behavior during operation. This
paper presents a numerically stable method for learning the parameters of jump
Markov linear systems using the expectation-maximisation (EM) approach. The
solution provided herein is a deterministic algorithm, and is not a Monte Carlo
based technique. As a result, simulations show that when compared to
alternative approaches, a more likely set of system parameters can be found
within a fixed computation time, which better explain the observations of the
system.
