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Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief introduction to the area, the book covers semi-Markov models for different control strategies in one-component systems, defining their stationary characteristics of reliability and efficiency, and uti

System analysis --- Markov processes. --- Mathematical models. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes

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This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature. In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.

Mathematical statistics --- Markov processes. --- Bayesian statistical decision theory. --- Nonparametric statistics. --- Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes

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Two-armed response-adaptive clinical trials are modelled as Markov decision problems to pursue two overriding objectives: Firstly, to identify the superior treatment at the end of the trial and, secondly, to keep the number of patients receiving the inferior treatment small. Such clinical trial designs are very important, especially for rare diseases. Thomas Ondra presents the main solution techniques for Markov decision problems and provides a detailed description how to obtain optimal allocation sequences. Contents Introduction to Markov Decision Problems and Examples Finite and Infinite Horizon Markov Decision Problems Solution Algorithms: Backward Induction, Value Iteration and Policy Iteration Designing Response Adaptive Clinical Trials with Markov Decision Problems Target Groups Researchers and students in the fields of mathematics and statistics Professionals in the pharmaceutical industry< The Author Thomas Ondra obtained his Master of Science degree in mathematics at University of Vienna. He is a research assistant and PhD student at the Section for Medical Statistics of Medical University of Vienna.  .

Mathematics. --- Computational Mathematics and Numerical Analysis. --- Probability Theory and Stochastic Processes. --- Analysis. --- Global analysis (Mathematics). --- Computer science --- Distribution (Probability theory). --- Mathématiques --- Analyse globale (Mathématiques) --- Informatique --- Distribution (Théorie des probabilités) --- Computer science -- Mathematics. --- Mathematics --- Physical Sciences & Mathematics --- Mathematics - General --- Markov processes. --- Statistical decision. --- Decision problems --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Mathematical analysis. --- Analysis (Mathematics). --- Computer mathematics. --- Probabilities. --- Game theory --- Operations research --- Statistics --- Management science --- Stochastic processes --- Distribution (Probability theory. --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- 517.1 Mathematical analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Probability Theory. --- Data processing.

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This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Acoustics in engineering. --- Social sciences --- Signal, Image and Speech Processing. --- Engineering Acoustics. --- Computer Appl. in Social and Behavioral Sciences. --- Data processing. --- Automatic speech recognition. --- Markov processes --- Mathematical models. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Mechanical speech recognizer --- Speech recognition, Automatic --- Pattern recognition systems --- Perceptrons --- Speech, Intelligibility of --- Speech perception --- Speech processing systems --- Signal processing. --- Image processing. --- Speech processing systems. --- Acoustical engineering. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Acoustic engineering --- Sonic engineering --- Sonics --- Sound engineering --- Sound-waves --- Engineering --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Industrial applications