Wahrscheinlichkeitstheorie und Stochastische Prozesse

Author: Michael Mürmann
Publisher: Springer-Verlag
ISBN: 364238160X
Format: PDF
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Dieses Lehrbuch beschäftigt sich mit den zentralen Gebieten einer maßtheoretisch orientierten Wahrscheinlichkeitstheorie im Umfang einer zweisemestrigen Vorlesung. Nach den Grundlagen werden Grenzwertsätze und schwache Konvergenz behandelt. Es folgt die Darstellung und Betrachtung der stochastischen Abhängigkeit durch die bedingte Erwartung, die mit der Radon-Nikodym-Ableitung realisiert wird. Sie wird angewandt auf die Theorie der stochastischen Prozesse, die nach der allgemeinen Konstruktion aus der Untersuchung von Martingalen und Markov-Prozessen besteht. Neu in einem Lehrbuch über allgemeine Wahrscheinlichkeitstheorie ist eine Einführung in die stochastische Analysis von Semimartingalen auf der Grundlage einer geeigneten Stetigkeitsbedingung mit Anwendungen auf die Theorie der Finanzmärkte. Das Buch enthält zahlreiche Übungen, teilweise mit Lösungen. Neben der Theorie vertiefen Anmerkungen, besonders zu mathematischen Modellen für Phänomene der Realität, das Verständnis.​

Inference in Hidden Markov Models

Author: Olivier Cappé
Publisher: Springer Science & Business Media
ISBN: 0387289828
Format: PDF, Kindle
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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 and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.

Semi Markov Chains and Hidden Semi Markov Models toward Applications

Author: Vlad Stefan Barbu
Publisher: Springer Science & Business Media
ISBN: 0387731733
Format: PDF, Mobi
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Here is a work that adds much to the sum of our knowledge in a key area of science today. It is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. A unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers.

Finite Mixture and Markov Switching Models

Author: Sylvia Frühwirth-Schnatter
Publisher: Springer Science & Business Media
ISBN: 0387357688
Format: PDF, Docs
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The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Statistical Inference for Discrete Time Stochastic Processes

Author: M. B. Rajarshi
Publisher: Springer Science & Business Media
ISBN: 8132207629
Format: PDF, ePub
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This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.

Mustererkennung mit Markov Modellen

Author: Gernot A. Fink
Publisher: Springer-Verlag
ISBN: 3322800652
Format: PDF, ePub, Docs
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Mustererkennung bildet die Grundlage für die Lösung verschiedenster Problembereiche in der Informatik. Spracherkennung, Schrifterkennung, Analyse biologischer Sequenzen: mit diesem Lehrbuch gelingt Ihnen der fundierte Einstieg in Theorie und Praxis.

Logische Grundlagen der K nstlichen Intelligenz

Author: Nils J. Nilsson
Publisher: Springer-Verlag
ISBN: 3322928810
Format: PDF, ePub
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Das Buch ist die deutsche Übersetzung des Standardwerkes der Stanforder Professoren Michael R. Genesereth und Nils J. Nilsson.Im Unterschied zu deutschen Lehrbüchern der Informatik zeichnet sich das Buch dadurch aus, daß es einen gut lesbaren Überblick gibt, ohne allzu formalistisch zu werden, gleichwohl aber von hohem Niveau ist und die Ergebnisse jüngster Forschung berücksichtigt. Das Buch empfiehlt sich sowohl für Studenten und Dozenten der Inf ormatik, aber auch für Forscher aus anderen Gebieten, die von den Grundlagen der Künstlichen Intelligenz profitieren möchten.