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Dynamic Probabilistic Systems Markov Models
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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 2007-06-05 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated work in two volumes, this text teaches readers to formulate, analyze, and evaluate Markov models. The first volume treats basic process; the second, semi-Markov and decision processes. 1971 edition.
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 . This book was released on 1971 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
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.
Book Synopsis Dynamic Probabilistic Systems: Semi-Markov and decision processes by : Ronald A. Howard
Download or read book Dynamic Probabilistic Systems: Semi-Markov and decision processes written by Ronald A. Howard and published by John Wiley & Sons. This book was released on 1971 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Markov Models written by Ronald A. Howard and published by . This book was released on 1971 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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:
Book Synopsis Dynamic Probabilistic Systems with Continuous Parameter Markov Chains and Semi-Markov Processes by :
Download or read book Dynamic Probabilistic Systems with Continuous Parameter Markov Chains and Semi-Markov Processes written by and published by . This book was released on 1973 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Book Synopsis Markov Chains: Models, Algorithms and Applications by : Wai-Ki Ching
Download or read book Markov Chains: Models, Algorithms and Applications written by Wai-Ki Ching and published by Springer Science & Business Media. This book was released on 2006-06-05 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
Book Synopsis Hidden Markov Models by : Przemyslaw Dymarski
Download or read book Hidden Markov Models written by Przemyslaw Dymarski and published by BoD – Books on Demand. This book was released on 2011-04-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
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
Download or read book Probability written by Steven Taylor and published by Steven Taylor. This book was released on 2020-09-09 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Book Bundle of Probability with Permutations and Markov Models Get two books in one now!! Probability with Permutations: An Introduction to Probability and Combinations Understanding probability as unique and stimulating theory which goes beyond conventional mathematics, will give you better perspective of the world around you. The first part of the book explains the fundamentals of probability in clear and easy to understand way even if you are not familiar with mathematics at all and you are just starting your journey towards this particular field of science. In the following sections of the book, the subject is explained in wider context along with importance of permutations and combinations in probability and their applications to a variety of scientific problems as well as the importance of probability in real life situations. Markov Models: An Introduction to Markov Models This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling. Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models. Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits. By Downloading This Book Bundle Now You Will Discover: History of Probability Explanation of Combinations Probability Using Permutations and Combinations Urn Problems Probability and Lottery Probability and Gambling Applications of Probability Hidden Markov Models Dynamic Bayesian Networks Stepwise Mutations using the Wright Fisher Model Using Normalized Algorithms to Update the Formulas Types of Markov Processes Important Tools used with HMM Machine Learning And much much more! Download this book bundle now and learn more about Probability with Permutations and Markov Models!
Book Synopsis Discrete-Time Markov Chains by : G. George Yin
Download or read book Discrete-Time Markov Chains written by G. George Yin and published by Springer Science & Business Media. This book was released on 2005-10-04 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on two-time-scale Markov chains in discrete time. Our motivation stems from existing and emerging applications in optimization and control of complex systems in manufacturing, wireless communication, and ?nancial engineering. Much of our e?ort in this book is devoted to designing system models arising from various applications, analyzing them via analytic and probabilistic techniques, and developing feasible compu- tionalschemes. Ourmainconcernistoreducetheinherentsystemcompl- ity. Although each of the applications has its own distinct characteristics, all of them are closely related through the modeling of uncertainty due to jump or switching random processes. Oneofthesalientfeaturesofthisbookistheuseofmulti-timescalesin Markovprocessesandtheirapplications. Intuitively,notallpartsorcom- nents of a large-scale system evolve at the same rate. Some of them change rapidly and others vary slowly. The di?erent rates of variations allow us to reduce complexity via decomposition and aggregation. It would be ideal if we could divide a large system into its smallest irreducible subsystems completely separable from one another and treat each subsystem indep- dently. However, this is often infeasible in reality due to various physical constraints and other considerations. Thus, we have to deal with situations in which the systems are only nearly decomposable in the sense that there are weak links among the irreducible subsystems, which dictate the oc- sional regime changes of the system. An e?ective way to treat such near decomposability is time-scale separation. That is, we set up the systems as if there were two time scales, fast vs. slow. xii Preface Followingthetime-scaleseparation,weusesingularperturbationmeth- ology to treat the underlying systems.
Book Synopsis Semi-Markov Models by : Jacques Janssen
Download or read book Semi-Markov Models written by Jacques Janssen and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the result of the International Symposium on Semi Markov Processes and their Applications held on June 4-7, 1984 at the Universite Libre de Bruxelles with the help of the FNRS (Fonds National de la Recherche Scientifique, Belgium), the Ministere de l'Education Nationale (Belgium) and the Bernoulli Society for Mathe matical Statistics and Probability. This international meeting was planned to make a state of the art for the area of semi-Markov theory and its applications, to bring together researchers in this field and to create a platform for open and thorough discussion. Main themes of the Symposium are the first ten sections of this book. The last section presented here gives an exhaustive biblio graphy on semi-Markov processes for the last ten years. Papers selected for this book are all invited papers and in addition some contributed papers retained after strong refereeing. Sections are I. Markov additive processes and regenerative systems II. Semi-Markov decision processes III. Algorithmic and computer-oriented approach IV. Semi-Markov models in economy and insurance V. Semi-Markov processes and reliability theory VI. Simulation and statistics for semi-Markov processes VII. Semi-Markov processes and queueing theory VIII. Branching IX. Applications in medicine X. Applications in other fields v PREFACE XI. A second bibliography on semi-Markov processes It is interesting to quote that sections IV to X represent a good sample of the main applications of semi-Markov processes i. e.
Book Synopsis Dynamic Probabilistic Models and Social Structure by : Guillermo L. Gómez M.
Download or read book Dynamic Probabilistic Models and Social Structure written by Guillermo L. Gómez M. and published by Springer Science & Business Media. This book was released on 1992 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph deals with the application of stochastic analysis and control techniques to the study of socioeconomic problems arising in processes of development and growth. Economic issues are formulated in a manner amenable to rigorous mathematical scrutiny without losing sight of their essential content. This self-contained work consists of three parts. Part I is a detailed examination of basic themes of available theories of political economy and develops a body of theoretical tools appropriate to the investigation of stochastic accumulation of capital within the framework of social reproduction. Part II points to the elaboration of a novel approach using probabilistic and variational methods. Key concepts such as expansion of capacity, development of productive forces, generation of economic surplus and effective demand are incorporated in stochastic models where learning, renewal of system potentials and conflicting social arrangements are at the heart of capital accumulation. Part III consists of four succinct surveys which carefully introduce the basic machinery from the theory of systems, (deterministic) optimal control, stochastic analysis and control of random systems. The last survey identifies distinguishing features of a labor-surplus economy of the peripheral type within the world capitalist system. The application of the semimartingale methodology and analogies from probabilistic mechanisc shifts informational structures to the forefront and points to fundamental links between microscopic and macroscopic aspects of system interactions as well as time scalling and observation of socioeconomic phenomena.
Book Synopsis Semi-Markov Models and Applications by : Jacques Janssen
Download or read book Semi-Markov Models and Applications written by Jacques Janssen and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a selection of papers presented to the Second Inter national Symposium on Semi-Markov Models: Theory and Applications held in Compiegne (France) in December 1998. This international meeting had the same aim as the first one held in Brussels in 1984 : to make, fourteen years later, the state of the art in the field of semi-Markov processes and their applications, bring together researchers in this field and also to stimulate fruitful discussions. The set of the subjects of the papers presented in Compiegne has a lot of similarities with the preceding Symposium; this shows that the main fields of semi-Markov processes are now well established particularly for basic applications in Reliability and Maintenance, Biomedicine, Queue ing, Control processes and production. A growing field is the one of insurance and finance but this is not really a surprising fact as the problem of pricing derivative products represents now a crucial problem in economics and finance. For example, stochastic models can be applied to financial and insur ance models as we have to evaluate the uncertainty of the future market behavior in order, firstly, to propose different measures for important risks such as the interest risk, the risk of default or the risk of catas trophe and secondly, to describe how to act in order to optimize the situation in time. Recently, the concept of VaR (Value at Risk) was "discovered" in portfolio theory enlarging so the fundamental model of Markowitz.
