TOC:
Foreword.
- Prerequisites in
probability calculus.
- Information and
the Kullback Distance.
- Probabilistic Models
and Learning.
- EM Algorithm.
- Alignment and Scoring.
- Mixture Models
and Profiles.
- Markov Chains.
- Learning of Markov
Chains.
- Markovian Models
for DNA sequences.
- Hidden Markov Models:
an Overview.
- HMM for DNA Sequences.
- Left to Right HMM
for Sequences.
- Derin's Algorithm.
- Forward - Backward
Algorithm.
- Baum - Welch Learning
Algorithm.
- Limit Points of
Baum - Welch.
- Asymptotics of
Learning.
- Full Probabilistic
HMM.
Index.
Hardbound, 416 pages;
November 2001; Kluwer Academic Publisher |