Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems?

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 ...

We consider penalized estimation in hidden Markov models (HMMs) with multivariate Normal observations. In the moderate-to-large dimensional setting, estimation for HMMs remains challenging in practice ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 36, No. 4 (December/décembre 2008), pp. 505-520 (16 pages) The authors consider hidden Markov models (HMMs) whose latent ...

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: ...

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a hidden state Markov model (HMM) that incorporates workers’ unobserved labor market attachment into ...

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