Equivalence and Reduction of Hidden Markov Models

Title: en_US Equivalence and Reduction of Hidden Markov Models
Creator: en_US Balasubramanian, Vijay
Date: 2004-10-20T19:55:31Z
Date Available: 2004-10-20T19:55:31Z
Date Issued: en_US 1993-01-01
Identifier: en_US AITR-1370
uri: http://hdl.handle.net/1721.1/6801
Abstract: en_US This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.
Extent: en_US 111 p.; 339883 bytes; 1337526 bytes
Format: application/octet-stream; application/pdf
Language: en_US
Relation: en_US AITR-1370
Subject: en_US Hideen Markov Models; en_US minimazation; en_US statistical modelling; en_US sstochastic processes