The Moments of Matched and Mismatched Hidden Markov Models
Abstract
An algorithm for computing the moments of matched and mismatched hidden Markov models from their defining parameters is presented. The ability of the first two moments to adequately describe the probability density function of a maximum posterior likelihood classifier based on hidden Markov models is assessed by examples. These examples include ergodic and nonergodic simulated hidden Markov observations that are matched and mismatched with the posterior likelihood classifier. One example discusses the effect of a noisy discrete communication channel on the posterior likelihood classifier reliability. The examples indicate that the posterior likelihood function is log-normal when the Markov chains are ergodic, and thus the first two moments suffice to describe the required probability density functions. The examples are also of independent interest because they indicate how different internal structures of hidden Markov models impact the performance of a maximum posterior likelihood classifier. Keywords: Statistical models, Sonar, Nonstationary time, sense or signals, Speech applications, Word recognition.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 11, 1987
- Accession Number
- ADA228892
Entities
People
- Roy L. Streit
Organizations
- Naval Underwater Systems Center