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.
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.
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.
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.
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:
Download or read book Dynamic probabilistic systems written by Ronald A. Howard and published by . This book was released on 1971 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Processes in Dynamic Probabilistic Systems written by A V Gheorghe and published by . This book was released on 1990-07-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Markov Models written by and published by . This book was released on 1971 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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:
Download or read book Probabilistic Models for Dynamical Systems written by Haym Benaroya and published by CRC Press. This book was released on 2013-05-02 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, Probabilistic Models for Dynamical Systems expands on the subject of probability theory. Written as an extension to its predecessor, this revised version introduces students to the randomness in variables and time dependent functions, and allows them to solve governing equations.Introduces probabilistic modeling and explo
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 Dynamic Probabilistic Models and Social Structure written by Guillermo L. Gómez M. and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models have been very successful in the study of the physical world. Galilei and Newton introduced point particles moving without friction under the action of simple forces as the basis for the description of concrete motions like the ones of the planets. This approach was sustained by appro priate mathematical methods, namely infinitesimal calculus, which was being developed at that time. In this way classical analytical mechanics was able to establish some general results, gaining insight through explicit solution of some simple cases and developing various methods of approximation for handling more complicated ones. Special relativity theory can be seen as an extension of this kind of modelling. In the study of electromagnetic phenomena and in general relativity another mathematical model is used, in which the concept of classical field plays the fundamental role. The equations of motion here are partial differential equations, and the methods of study used involve further developments of classical analysis. These models are deterministic in nature. However it was realized already in the second half of last century, through the work of Maxwell, Boltzmann, Gibbs and others, that in the discussion of systems involving a great number of particles, the deterministic description is not by itself of great help, in particu lar a suitable "weighting" of all possible initial conditions should be considered.
Download or read book Probabilistic Systems Analysis written by Arthur M. Breipohl and published by . This book was released on 1970 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elementary probability; Engineering applications of probability; Random variables; Expected values; Distribution of functions of Random variables; Applications of Random variables to systems problems; Distributions from data; Estimation; Engineering decisions; Introduction to Random processes; Systems and Random signals.
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:
Download or read book Formal Methods for Real Time and Probabilistic Systems written by Jost-Pieter Katoen and published by Springer. This book was released on 2003-05-21 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Fifth International AMAST Workshop on Formal Methods for Real-Time and Probabilistic Systems, ARTS '99, held in Bamberg, Germany in May 1999. The 17 revised full papers presented together with three invited contributions were carefully reviewed and selected from 33 submissions. The papers are organized in topical sections on verification of probabilistic systems, model checking for probabilistic systems, semantics of probabilistic process calculi, semantics of real-time processes, real-time compilation, stochastic process algebra, and modeling and verification of real-time systems.
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.
Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
