MARC details
000 -LEADER |
fixed length control field |
04347cam a2200253 i 4500 |
001 - CONTROL NUMBER |
control field |
136637 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
ISI Library, Kolkata |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20160314154727.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
140325t20142014njua b 001 0 eng |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9780691133157 (hardcover : acidfree paper) |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
ISI Library |
Language of cataloging |
eng |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
570.285 |
Edition number |
23 |
Item number |
V655 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Vidyasagar, M. |
245 10 - TITLE STATEMENT |
Title |
Hidden Markov processes : |
Remainder of title |
theory and applications to biology / |
Statement of responsibility, etc |
M. Vidyasagar. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
Place of publication, distribution, etc |
Princeton : |
Name of publisher, distributor, etc |
Princeton University Press, |
Date of publication, distribution, etc |
c2014. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
xiv, 287 p. : |
Other physical details |
illustrations; |
Dimensions |
24 cm. |
490 0# - SERIES STATEMENT |
Series statement |
Princeton series in applied mathematics |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc |
Includes bibliographical references and index. |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
1. Introduction to probability and random variables : Introduction to random variables ; Motivation ; Definition of a random variable and probability ; Function of a random variable, expected value ; Total variation distance ; Multiple random variables ; Joint and marginal distributions ; Independence and conditional distributions ; Bayes' rule ; MAP and maximum likelihood estimates ; Random variables assuming infinitely many values ; Some preliminaries ; Markov and Chebycheff inequalities ; Hoeffding's inequality ; Monte Carlo simulation ; Introduction to Cramer's theorem --<br/> 2. Introduction information theory : Convex and concave functions ; Entropy ; Definition of entropy ; Properties of the entropy function ; Conditional entropy ; Uniqueness of the entropy function ; Relative entropy and the Kullback-Leibler divergence --<br/> 3. Nonnegative matrices : Canonical form for nonnegative matrices ; Basic version of the canonical form ; Irreducible matrices ; Final version of canonical form ; Irreducibility, aperiodicity, and primitivity ; Canonical form for periodic irreducible matrices ; Perron-Frobenius theory ; Perron-Frobenius theorem for primitive matrices ; Perron-Frobenius theorem for irreducible matrices. <br/>4. Markov processes : Basic definitions ; The Markov property and the state transition matrix ; Estimating the state transition matrix ; Dynamics of stationary Markov chains ; Recurrent and transient states; Hitting probabilities and mean hitting times ; Ergodicity of Markov chains --<br/> 5. Introduction to large deviation theory : Problem formulation ; large deviation property for I.I.D. samples: Sanov's theorem ; Large deviation property for Markov chains ; Stationary distributions ; Entropy and relative entropy rates ; The rate function for Doubleton frequencies ; The rate function for Singleton frequencies --<br/> 6. Hidden Markov processes: basic properties : Equivalence of various hidden Markov models ; Three different-looking models ; Equivalence between the three models ; Computation of likelihoods ; Computation of likelihoods of output sequences ; The Viterbi algorithm ; The Baum-Welch algorithm --<br/> 7. Hidden Markov processes: the complete realization problem : Finite Hankel rank: a universal necessary condition ; Nonsufficiency of the finite Hankel rank condition ; An abstract necessary and sufficient condition ; Existence of regular quasi-realizations ; Spectral properties of alpha-mixing processes ; Ultra-mixing processes ; A sufficient condition for the existence of HMMs. 8. Some applications to computational biology : Some basic biology ; The genome ; The genetic code ; Optimal gapped sequence alignment ; Problem formulation ; Solution via dynamic programming ; Gene finding ; Genes and the gene-finding problem ; The GLIMMER family of algorithms ; The GENSCAN algorithm ; Protein classification ; Proteins and the protein classification problem ; Protein classification using profile hidden Markov models --<br/> 9. BLAST theory : BLAST theory: statements of main results ; Problem formulations ; THe moment generating function ; Statement of main results ; Application of main results ; BLAST theory: proofs of main results --<br/>Bibliography --<br/>Index. |
520 ## - SUMMARY, ETC. |
Summary, etc |
Explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. This book provides a range of exercises, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Computational biology. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Markov processes. |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Dewey Decimal Classification |
Koha item type |
Books |