Viterbi Algorithm: Dynamic Programming, Algorithm, Likelihood Function, Markov Information Source, Hidden Markov Model, Information Theory, Error Detection and Correction - Softcover

 
9786130355128: Viterbi Algorithm: Dynamic Programming, Algorithm, Likelihood Function, Markov Information Source, Hidden Markov Model, Information Theory, Error Detection and Correction
Softcover
ISBN 10: 6130355122 ISBN 13: 9786130355128
Publisher: Betascript Publishers, 2010

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, especially in the context of Markov information sources, and more generally, hidden Markov models. The forward algorithm is a closely related algorithm for computing the probability of a sequence of observed events. These algorithms belong to the realm of information theory. The algorithm makes a number of assumptions. First, both the observed events and hidden events must be in a sequence. This sequence often corresponds to time. Second, these two sequences need to be aligned, and an instance of an observed event needs to correspond to exactly one instance of a hidden event. Third, computing the most likely hidden sequence up to a certain point t must depend only on the observed event at point t, and the most likely sequence at point t − 1. These assumptions are all satisfied in a first-order hidden Markov model.

"synopsis" may belong to another edition of this title.

Reseña del editor

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, especially in the context of Markov information sources, and more generally, hidden Markov models. The forward algorithm is a closely related algorithm for computing the probability of a sequence of observed events. These algorithms belong to the realm of information theory. The algorithm makes a number of assumptions. First, both the observed events and hidden events must be in a sequence. This sequence often corresponds to time. Second, these two sequences need to be aligned, and an instance of an observed event needs to correspond to exactly one instance of a hidden event. Third, computing the most likely hidden sequence up to a certain point t must depend only on the observed event at point t, and the most likely sequence at point t − 1. These assumptions are all satisfied in a first-order hidden Markov model.

"About this title" may belong to another edition of this title.

Publisher
Betascript Publishers
Publication date
2010
Language
English
ISBN 10 
6130355122
ISBN 13 
9786130355128
Binding
Tapa blanda
Number of pages
124
Editor
Surhone Lambert M., Timpledon Miriam T., Marseken Susan F.