Inference in Hidden Markov Models [electronic resource] / by Olivier Cappé, Eric Moulines, Tobias Rydén.
By: Cappé, Olivier [author.].
Contributor(s): Moulines, Eric [author.]  Rydén, Tobias [author.]  SpringerLink (Online service).
Material type: TextSeries: Springer Series in Statistics: Publisher: New York, NY : Springer New York, 2005Description: XVII, 653 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387289823.Subject(s): Distribution (Probability theory  Mathematical statistics  Statistics  Computer simulation  Probability Theory and Stochastic Processes  Statistical Theory and Methods  Signal, Image and Speech Processing  Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences  Statistics for Business/Economics/Mathematical Finance/Insurance  Simulation and ModelingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.2 Online resources: Click here to access onlineItem type  Current location  Call number  Status  Date due  Barcode  Item holds  

EBOOKS 
ISI Library, Kolkata

Available  EB1158 
Main Definitions and Notations  Main Definitions and Notations  State Inference  Filtering and Smoothing Recursions  Advanced Topics in Smoothing  Applications of Smoothing  Monte Carlo Methods  Sequential Monte Carlo Methods  Advanced Topics in Sequential Monte Carlo  Analysis of Sequential Monte Carlo Methods  Parameter Inference  Maximum Likelihood Inference, Part I: Optimization Through Exact Smoothing  Maximum Likelihood Inference, Part II: Monte Carlo Optimization  Statistical Properties of the Maximum Likelihood Estimator  Fully Bayesian Approaches  Background and Complements  Elements of Markov Chain Theory  An InformationTheoretic Perspective on Order Estimation.
Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, bioinformatics, finance and many more. This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called statespace models) requiring approximate simulationbased algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear statespace models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models. This volume will suit anybody with an interest in inference for stochastic processes, and it will be useful for researchers and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The algorithmic parts of the book do not require an advanced mathematical background, while the more theoretical parts require knowledge of probability theory at the measuretheoretical level. Olivier Cappé is Researcher for the French National Center for Scientific Research (CNRS). He received the Ph.D. degree in 1993 from Ecole Nationale Supérieure des Télécommunications, Paris, France, where he is currently a Research Associate. Most of his current research concerns computational statistics and statistical learning. Eric Moulines is Professor at Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France. He graduated from Ecole Polytechnique, France, in 1984 and received the Ph.D. degree from ENST in 1990. He has authored more than 150 papers in applied probability, mathematical statistics and signal processing. Tobias Rydén is Professor of Mathematical Statistics at Lund University, Sweden, where he also received his Ph.D. in 1993. His publications include papers ranging from statistical theory to algorithmic developments for hidden Markov models.
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Inference in hidden markov models by Cappe Olivier 
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