Semi-Markov Chains and Hidden Semi-Markov Models toward Applications
Semi-Markov Chains and Hidden Semi-Markov Models toward Applications

Author: Vlad Stefan Barbu

Publisher: Springer Science & Business Media

Published: 2009-01-07

Total Pages: 233

ISBN-13: 0387731733

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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.

Introduction to Hidden Semi-Markov Models
Introduction to Hidden Semi-Markov Models

Author: John Van der Hoek

Publisher: Cambridge University Press

Published: 2018

Total Pages: 185

ISBN-13: 1108421601

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Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications

Introduction to Hidden Semi-Markov Models
Introduction to Hidden Semi-Markov Models

Author: John van der Hoek

Publisher: Cambridge University Press

Published: 2018-02-08

Total Pages: 237

ISBN-13: 1108381987

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Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications.

Statistical Topics and Stochastic Models for Dependent Data with Applications
Statistical Topics and Stochastic Models for Dependent Data with Applications

Author: Vlad Stefan Barbu

Publisher: John Wiley & Sons

Published: 2020-12-03

Total Pages: 288

ISBN-13: 1786306034

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This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.

Hidden Semi-Markov Models
Hidden Semi-Markov Models

Author: Shun-Zheng Yu

Publisher: Morgan Kaufmann

Published: 2015-10-22

Total Pages: 209

ISBN-13: 0128027711

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Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.

Handbook of Performability Engineering
Handbook of Performability Engineering

Author: Krishna B. Misra

Publisher: Springer Science & Business Media

Published: 2008-08-24

Total Pages: 1331

ISBN-13: 1848001312

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Dependability and cost effectiveness are primarily seen as instruments for conducting international trade in the free market environment. These factors cannot be considered in isolation of each other. This handbook considers all aspects of performability engineering. The book provides a holistic view of the entire life cycle of activities of the product, along with the associated cost of environmental preservation at each stage, while maximizing the performance.

Hidden Markov Models
Hidden Markov Models

Author: Robert J Elliott

Publisher: Springer Science & Business Media

Published: 2008-09-27

Total Pages: 374

ISBN-13: 0387848541

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As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

Markov Processes and Applications
Markov Processes and Applications

Author: Etienne Pardoux

Publisher: John Wiley & Sons

Published: 2008-11-20

Total Pages: 322

ISBN-13: 0470721863

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"This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications. The mathematical treatment is precise and rigorous without superfluous details, and the results are immediately illustrated in illuminating examples. This book will be extremely useful to anybody teaching a course on Markov processes." Jean-François Le Gall, Professor at Université de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields. After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance. Features include: The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes. An introduction to diffusion processes, mathematical finance and stochastic calculus. Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science. Numerous exercises and problems with solutions to most of them

Reliability Engineering
Reliability Engineering

Author: Ilia Vonta

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 200

ISBN-13: 135113034X

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Reliability theory is a multidisciplinary science aimed at developing complex systems that are resistant to failures. Reliability engineering has emerged as a main field not only for scientists and researchers, but also for engineers and industrial managers. This book covers the recent developments in reliability engineering. It presents new theoretical issues that were not previously published, as well as the solutions of practical problems and case studies illustrating the applications methodology. This book is written by a number of leading scientists, analysts, mathematicians, statisticians, and engineers who have been working on the front end of reliability science and engineering. Reliability Engineering: Theory and Applications covers the recent developments in reliability engineering. It presents new theoretical issues that were not previously presented in the literature, as well as the solutions of important practical problems and case studies illustrating the applications methodology. Features Covers applications to reliability engineering practice Discusses current advances and developments Introduces current achievements in the field Considers and analyses case studies along with real world examples Presents numerous examples to illustrate the theoretical results