scholarly journals Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

2011 ◽  
pp. 59-66 ◽  
Author(s):  
Wei Liu ◽  
Suyash P. Awate ◽  
Jeffrey S. Anderson ◽  
Deborah Yurgelun-Todd ◽  
P. Thomas Fletcher
Keyword(s):  
Monte Carlo ◽  
Hidden Markov Models ◽  
Expectation Maximization ◽  
Resting State ◽  
Markov Models ◽  
Hidden Markov ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Monte Carlo Expectation Maximization

scholarly journals Smooth On-Line Learning Algorithms for Hidden Markov Models

1994 ◽  
Vol 6 (2) ◽  
pp. 307-318 ◽  
Author(s):  
Pierre Baldi ◽  
Yves Chauvin
Keyword(s):  
Hidden Markov Models ◽  
Expectation Maximization ◽  
Markov Models ◽  
Learning Algorithm ◽  
Hidden Markov ◽  
Batch Mode ◽  
On Line ◽  
Maximization Algorithms ◽  
On Line Learning ◽  
Entropy Functions

A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each example presentation) or in batch mode, with or without the usual Viterbi most likely path approximation. The algorithms have simple expressions that result from using a normalized-exponential representation for the HMM parameters. All the algorithms presented are proved to be exact or approximate gradient optimization algorithms with respect to likelihood, log-likelihood, or cross-entropy functions, and as such are usually convergent. These algorithms can also be casted in the more general EM (Expectation-Maximization) framework where they can be viewed as exact or approximate GEM (Generalized Expectation-Maximization) algorithms. The mathematical properties of the algorithms are derived in the appendix.

Sign in / Sign up

Export Citation Format

Share Document