000			05113nam a22005175i 4500
001			978-0-387-28982-3
003			DE-He213
005			20250710083943.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387289823 _a99780387289823
024	7		_a10.1007/0-387-28982-8 _2doi
082	0	4	_a519.5 _223
100	1		_aCappé, Olivier. _eauthor.
245	1	0	_aInference in Hidden Markov Models _h[recurso electrónico] / _cby Olivier Cappé, Eric Moulines, Tobias Rydén.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXVIII, 654 p. 78 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aMain 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 Information-Theoretic Perspective on Order Estimation.
520			_aHidden 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 state-space models) requiring approximate simulation-based 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 state-space 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 measure-theoretical 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.
650		0	_aSTATISTICS.
650		0	_aCOMPUTER SIMULATION.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aECONOMICS _xSTATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aSIGNAL, IMAGE AND SPEECH PROCESSING.
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
650	2	4	_aSTATISTICS FOR BUSINESS/ECONOMICS/MATHEMATICAL FINANCE/INSURANCE.
650	2	4	_aSIMULATION AND MODELING.
650	2	4	_aCONTROL ENGINEERING.
700	1		_aMoulines, Eric. _eauthor.
700	1		_aRydén, Tobias. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387402642
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-28982-8 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56903 _d56903