  In this paper, we develop methods of nonlinear filtering and prediction of an
unobservable Markov chain with a finite set of states. This Markov chain
controls coefficients of AR(p) model. Using observations generated by AR(p)
model we have to estimate the state of Markov chain in the case of an unknown
probability transition matrix. Comparison of proposed non-parametric algorithms
with the optimal methods in the case of the known transition matrix is carried
out by simulating.
