Minimal Realization Problems for Hidden Markov Models

The Realization Problem for Hidden Markov Models

Ergodic Hidden Markov Models for Workload Characterization Problems

We present a novel approach for accurate characterization of workloads. Workloads are generally described with statistical models and are based on the analysis of resource requests measurements of a running program. In this paper we propose to consider the sequence of virtual memory references generated from a program during its execution as a temporal series, and to use spectral analysis princ...

Hidden Markov Models for Images

We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. We show that the Viterbi algorithm approach used for segmenting Markov chains can be extended to Markov meshes. The segmental k-means algorithm can then be applied to iteratively estimate the state transition matrix and the probability densities of the observations for the model. We als...

Hidden Markov Models for Alcoholism

In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, with levels corresponding to abstinence, light drinking, and heavy drinking. In these trials, within-subject drinking patterns are often characterized by alternating periods of heavy drinking and ab...

Equations for hidden Markov models

In the following, we will outline how to obtain invariants for hidden Markov and related models, based on an approach which, in its most prevalent application, served to solve the identifiability problem for hidden Markov processes (HMPs) in 1992 [13]. Some of its foundations had been layed in the late 50’s and early 60’s in order to get a grasp of problems related to that of identifying HMPs [...