Fast, non-iterative estimation of Hidden Markov models

• The solution of many important signal processing problems depends on the estimation of the parameters of a Hidden Markov Model (HMM). Unfortunately, to date the only known methods for performing this estimation have been iterative, and therefore computationally demanding. By way of contrast, this paper presents a new fast and non-iterative method that utilizes certain recent 'state spaced subspace system identification' (4SID) ideas from the control theory literature. A short simulation example presented here indicates this new technique to be almost as accurate as Maximum-Likelihood estimation, but an order of magnitude less computationally demanding than the Baum-Welch (EM) algorithm.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Nyckelord

Algorithms

Computer simulation

Identification (control systems)

Markov processes

Mathematical models

Parameter estimation

Probability

Problem solving

Baum-Welch algorithm

Hidden Markov model

State spaced subspace system identification

Signal processing

Publikations- och innehållstyp

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