Hidden Markov models (HMMs) provide a powerful framework for inferring unobserved processes that evolve over time or space by linking an underlying Markovian state sequence to observed data via ...

The amino acid sequence of the transmembrane protein and its corresponding positions on the cell membrane are transformed into a hidden Markov process. After evaluating the parameters, the Viterbi ...

In this talk I consider sequential Monte Carlo (SMC) methods for hidden Markov models. In the scenario for which the conditional density of the observations given the latent state is intractable we ...