We present algorithms for state and parameter estimation of Markov modulated Poisson processes (MMPP). We first derive finite-dimensional innovations and Zakai filters for various statistics of a MMPP. Using these filters, a tilter-based expectation-maximization algorithm is derived for computing maximum-likelihood parameter estimates. Finally, we present an application of the techniques in image-based tracking by estimating the state of a Markovian jump linear system.