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 ...

Abstract Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot.

Rachel J. MacKay, Estimating the Order of a Hidden Markov Model, The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 30, No. 4 (Dec., 2002), pp. 573-589 ...

Hidden Markov models have an extensive history in a wide variety of pattern classification applications. In these models, an unobserved finite state Markov chain generates observed symbols whose ...

“A Hidden Markov Model Combined with Climate Indices for Multi-decadal Streamflow Simulation,” Water Resources Research, 50, 7836-7846. Abstract: Hydroclimate time series often exhibit very low ...

Hidden Markov Models (HMM) One characteristic of speech problems as well as chromosome karyotyping is that the vectors can be of variable length.