Dynamic Probabilistic Systems Markov Models

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Dynamic Probabilistic Systems, Volume I

Author : Ronald A. Howard
Publisher : Courier Corporation
ISBN 13 : 0486140679
Total Pages : 610 pages
Book Rating : 4.4/5 (861 download)

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Book Synopsis Dynamic Probabilistic Systems, Volume I by : Ronald A. Howard

Download or read book Dynamic Probabilistic Systems, Volume I written by Ronald A. Howard and published by Courier Corporation. This book was released on 2012-05-04 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, begins with the basic Markov model, proceeding to systems analyses of linear processes and Markov processes, transient Markov processes and Markov process statistics, and statistics and inference. Subsequent chapters explore recurrent events and random walks, Markovian population models, and time-varying Markov processes. Volume I concludes with a pair of helpful indexes.

Dynamic Probabilistic Systems, Volume II

Author : Ronald A. Howard
Publisher : Courier Corporation
ISBN 13 : 0486152006
Total Pages : 857 pages
Book Rating : 4.4/5 (861 download)

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Book Synopsis Dynamic Probabilistic Systems, Volume II by : Ronald A. Howard

Download or read book Dynamic Probabilistic Systems, Volume II written by Ronald A. Howard and published by Courier Corporation. This book was released on 2013-01-18 with total page 857 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, continues his treatment from Volume I with surveys of the discrete- and continuous-time semi-Markov processes, continuous-time Markov processes, and the optimization procedure of dynamic programming. The final chapter reviews the preceding material, focusing on the decision processes with discussions of decision structure, value and policy iteration, and examples of infinite duration and transient processes. Volume II concludes with an appendix listing the properties of congruent matrix multiplication.

Dynamic Probabilistic Systems: Markov models

Author : Ronald A. Howard
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 616 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Dynamic Probabilistic Systems: Markov models by : Ronald A. Howard

Download or read book Dynamic Probabilistic Systems: Markov models written by Ronald A. Howard and published by John Wiley & Sons. This book was released on 1971 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Hidden Markov Models and Dynamical Systems

Author : Andrew M. Fraser
Publisher : SIAM
ISBN 13 : 0898716659
Total Pages : 141 pages
Book Rating : 4.8/5 (987 download)

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Book Synopsis Hidden Markov Models and Dynamical Systems by : Andrew M. Fraser

Download or read book Hidden Markov Models and Dynamical Systems written by Andrew M. Fraser and published by SIAM. This book was released on 2008-01-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community.

Practical Probabilistic Programming

Author : Avi Pfeffer
Publisher : Simon and Schuster
ISBN 13 : 1638352372
Total Pages : 650 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Practical Probabilistic Programming by : Avi Pfeffer

Download or read book Practical Probabilistic Programming written by Avi Pfeffer and published by Simon and Schuster. This book was released on 2016-03-29 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you'll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The data you accumulate about your customers, products, and website users can help you not only to interpret your past, it can also help you predict your future! Probabilistic programming uses code to draw probabilistic inferences from data. By applying specialized algorithms, your programs assign degrees of probability to conclusions. This means you can forecast future events like sales trends, computer system failures, experimental outcomes, and many other critical concerns. About the Book Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you’ll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You’ll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you’ll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. What's Inside Introduction to probabilistic modeling Writing probabilistic programs in Figaro Building Bayesian networks Predicting product lifecycles Decision-making algorithms About the Reader This book assumes no prior exposure to probabilistic programming. Knowledge of Scala is helpful. About the Author Avi Pfeffer is the principal developer of the Figaro language for probabilistic programming. Table of Contents PART 1 INTRODUCING PROBABILISTIC PROGRAMMING AND FIGARO Probabilistic programming in a nutshell A quick Figaro tutorial Creating a probabilistic programming application PART 2 WRITING PROBABILISTIC PROGRAMS Probabilistic models and probabilistic programs Modeling dependencies with Bayesian and Markov networks Using Scala and Figaro collections to build up models Object-oriented probabilistic modeling Modeling dynamic systems PART 3 INFERENCE The three rules of probabilistic inference Factored inference algorithms Sampling algorithms Solving other inference tasks Dynamic reasoning and parameter learning

Decision Processes in Dynamic Probabilistic Systems

Author : A.V. Gheorghe
Publisher : Springer Science & Business Media
ISBN 13 : 9400904932
Total Pages : 370 pages
Book Rating : 4.4/5 (9 download)

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Book Synopsis Decision Processes in Dynamic Probabilistic Systems by : A.V. Gheorghe

Download or read book Decision Processes in Dynamic Probabilistic Systems written by A.V. Gheorghe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Et moi - ... - si j'avait su comment en revenir. One service mathematics has rendered the je n'y serais point aile: human race. It has put common sense back where it belongs. on the topmost shelf next Jules Verne (0 the dusty canister labelled 'discarded non sense'. The series is divergent; therefore we may be able to do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Handbook of Probabilistic Models

Author : Pijush Samui
Publisher : Butterworth-Heinemann
ISBN 13 : 0128165464
Total Pages : 592 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Handbook of Probabilistic Models by : Pijush Samui

Download or read book Handbook of Probabilistic Models written by Pijush Samui and published by Butterworth-Heinemann. This book was released on 2019-10-05 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Markov Chains and Stochastic Stability

Author : Sean Meyn
Publisher : Cambridge University Press
ISBN 13 : 0521731828
Total Pages : 623 pages
Book Rating : 4.5/5 (217 download)

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Book Synopsis Markov Chains and Stochastic Stability by : Sean Meyn

Download or read book Markov Chains and Stochastic Stability written by Sean Meyn and published by Cambridge University Press. This book was released on 2009-04-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996.

Markov Models & Optimization

Author : M.H.A. Davis
Publisher : CRC Press
ISBN 13 : 9780412314100
Total Pages : 316 pages
Book Rating : 4.3/5 (141 download)

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Book Synopsis Markov Models & Optimization by : M.H.A. Davis

Download or read book Markov Models & Optimization written by M.H.A. Davis and published by CRC Press. This book was released on 1993-08-01 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process that follows deterministic trajectories between random jumps, the latter occurring either spontaneously, in a Poisson-like fashion, or when the process hits the boundary of its state space. This formulation includes an enormous variety of applied problems in engineering, operations research, management science and economics as special cases; examples include queueing systems, stochastic scheduling, inventory control, resource allocation problems, optimal planning of production or exploitation of renewable or non-renewable resources, insurance analysis, fault detection in process systems, and tracking of maneuvering targets, among many others. The first part of the book shows how these applications lead to the PDP as a system model, and the main properties of PDPs are derived. There is particular emphasis on the so-called extended generator of the process, which gives a general method for calculating expectations and distributions of system performance functions. The second half of the book is devoted to control theory for PDPs, with a view to controlling PDP models for optimal performance: characterizations are obtained of optimal strategies both for continuously-acting controllers and for control by intervention (impulse control). Throughout the book, modern methods of stochastic analysis are used, but all the necessary theory is developed from scratch and presented in a self-contained way. The book will be useful to engineers and scientists in the application areas as well as to mathematicians interested in applications of stochastic analysis.

Controlled Markov Processes and Viscosity Solutions

Author : Wendell H. Fleming
Publisher : Springer Science & Business Media
ISBN 13 : 0387310711
Total Pages : 436 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Controlled Markov Processes and Viscosity Solutions by : Wendell H. Fleming

Download or read book Controlled Markov Processes and Viscosity Solutions written by Wendell H. Fleming and published by Springer Science & Business Media. This book was released on 2006-02-04 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It covers dynamic programming for deterministic optimal control problems, as well as to the corresponding theory of viscosity solutions. New chapters in this second edition introduce the role of stochastic optimal control in portfolio optimization and in pricing derivatives in incomplete markets and two-controller, zero-sum differential games.

Dynamic Programming and Markov Processes

Author : Ronald A. Howard
Publisher :
ISBN 13 :
Total Pages : 136 pages
Book Rating : 4.:/5 (5 download)

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Book Synopsis Dynamic Programming and Markov Processes by : Ronald A. Howard

Download or read book Dynamic Programming and Markov Processes written by Ronald A. Howard and published by . This book was released on 1969 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Secondary Analysis of Electronic Health Records

Author : MIT Critical Data
Publisher : Springer
ISBN 13 : 3319437429
Total Pages : 435 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Secondary Analysis of Electronic Health Records by : MIT Critical Data

Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Markov Decision Processes

Author : Martin L. Puterman
Publisher : John Wiley & Sons
ISBN 13 : 1118625870
Total Pages : 544 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Markov Decision Processes by : Martin L. Puterman

Download or read book Markov Decision Processes written by Martin L. Puterman and published by John Wiley & Sons. This book was released on 2014-08-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association

Probabilistic Graphical Models

Author : Luis Enrique Sucar
Publisher : Springer Nature
ISBN 13 : 3030619435
Total Pages : 370 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Probabilistic Graphical Models by : Luis Enrique Sucar

Download or read book Probabilistic Graphical Models written by Luis Enrique Sucar and published by Springer Nature. This book was released on 2020-12-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

Efficient Learning Machines

Author : Mariette Awad
Publisher : Apress
ISBN 13 : 1430259906
Total Pages : 263 pages
Book Rating : 4.4/5 (32 download)

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Book Synopsis Efficient Learning Machines by : Mariette Awad

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Basics of Applied Stochastic Processes

Author : Richard Serfozo
Publisher : Springer Science & Business Media
ISBN 13 : 3540893326
Total Pages : 452 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Basics of Applied Stochastic Processes by : Richard Serfozo

Download or read book Basics of Applied Stochastic Processes written by Richard Serfozo and published by Springer Science & Business Media. This book was released on 2009-01-24 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of large numbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerative increments. Extensive examples and exercises show how to formulate stochastic models of systems as functions of a system’s data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brownian approximations, and varied Markovian models. The technical level of the volume is between that of introductory texts that focus on highlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes.

Probabilistic Modelling

Author : I. Mitrani
Publisher : Cambridge University Press
ISBN 13 : 9780521585309
Total Pages : 226 pages
Book Rating : 4.5/5 (853 download)

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Book Synopsis Probabilistic Modelling by : I. Mitrani

Download or read book Probabilistic Modelling written by I. Mitrani and published by Cambridge University Press. This book was released on 1998 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic modelling is the most cost-effective means of performance and reliability evaluation of complex dynamic systems. This self-contained text will be welcomed by students and teachers for its no-nonsense treatment of the basic results and examples of their application. The only mathematical background that is assumed is basic calculus. The necessary fundamentals of probability theory are included, as well as an introduction to renewal, Poisson and Markov processes. Models arising in the fields of manufacturing, computing and communications, involving single or multiple service stations and one or more customer classes, are examined in some detail. Both exact and approximate solution methods are discussed, including recent techniques such as spectral expansion. Special attention is devoted to models of systems subject to breakdowns and repairs. Throughout the book, strong emphasis is placed on explaining the ideas behind the results and helping the reader to use them, making the book ideal for students in computer science, engineering or operations research taking courses in modern system design.