Hidden Markov models (HMMs) provide a robust statistical framework for analysing sequential data by assuming that the observed processes are driven by underlying, unobserved states. These models have ...

Finite mixture models and hidden Markov models (HMMs) occupy central roles in modern statistical inference and data analysis. Finite mixture models assume that data originate from a latent combination ...

Erkyihun S.T., E Zagona, B. Rajagopalan, (2017). “Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow,” Journal of Hydrologic Engineering 2017, 22(9): ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 58, No. 3 (Jul., 2009), pp. 405-426 (22 pages) A new hidden Markov model for the space-time evolution of daily rainfall is ...

C. Bracken, B. Rajagopalan, & E. Zagona (2014). “A Hidden Markov Model Combined with Climate Indices for Multi-decadal Streamflow Simulation,” Water Resources Research, 50, 7836-7846. Abstract: ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 26, No. 1 (Mar., 1998), pp. 107-125 (19 pages) We show how the concept of hidden Markov model may be accommodated in a ...