On some Extensions of the Sequential Monte Carlo methods in high-order Hidden Markov Models

  • Mouhamad M. Allaya
  • Alioune Coulibaly
  • El Hadji Deme
  • Mouhamadou M. Ka
  • Babacar Sene


We analyze some extensions of the Sequential Monte Carlo (SMC) methods in the context of nonlinear state space models. Namely, we tailor the SMC  methods to handle high-order HMM through the customary recursions of  posterior distributions. It proceeds on mimicking the two-step procedure that is, the prediction step and the update step, in the derivation of the filter  distribution. Once stated, we extend some smoothing recursions as the  Forward-Backward algorithm and the Backward smoother to deal with the actual smoothing distributions in high-order HMM. Finally, we give few examples as an application of these extensions.

Key words: Sequential Monte Carlo, high-order HMM, Smoothing, Filtering


Journal Identifiers

print ISSN: 2316-090X