Dynamic Probabilistic Systems Markov Models
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Dynamic Probabilistic Systems Volume I
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Author : Ronald A. Howard
language : en
Publisher: Courier Corporation
Release Date : 2007-06-05
Dynamic Probabilistic Systems Volume I written by Ronald A. Howard and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-05 with Mathematics categories.
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.
Dynamic Probabilistic Systems Markov Models
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Author : Ronald A. Howard
language : en
Publisher:
Release Date : 1971
Dynamic Probabilistic Systems Markov Models written by Ronald A. Howard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Markov processes categories.
Decision Processes In Dynamic Probabilistic Systems
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Author : A.V. Gheorghe
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Decision Processes In Dynamic Probabilistic Systems written by A.V. Gheorghe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
'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.
Dynamic Probabilistic Systems Volume Ii
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Author : Ronald A. Howard
language : en
Publisher: Courier Corporation
Release Date : 2013-01-18
Dynamic Probabilistic Systems Volume Ii written by Ronald A. Howard and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-18 with Mathematics categories.
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 Semi Markov And Decision Processes
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Author : Ronald A. Howard
language : en
Publisher: John Wiley & Sons
Release Date : 1971
Dynamic Probabilistic Systems Semi Markov And Decision Processes written by Ronald A. Howard and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Business & Economics categories.
Hidden Markov Models And Dynamical Systems
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Author : Andrew M. Fraser
language : en
Publisher: SIAM
Release Date : 2008-01-01
Hidden Markov Models And Dynamical Systems written by Andrew M. Fraser and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-01 with Mathematics categories.
Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community.
Decision Processes In Dynamic Probabilistic Systems
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Author : A V Gheorghe
language : en
Publisher:
Release Date : 1990-07-31
Decision Processes In Dynamic Probabilistic Systems written by A V Gheorghe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-07-31 with categories.
Markov Chains Models Algorithms And Applications
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Author : Wai-Ki Ching
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-06-05
Markov Chains Models Algorithms And Applications written by Wai-Ki Ching and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-05 with Mathematics categories.
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.
Discrete Time Markov Chains
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Author : G. George Yin
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-30
Discrete Time Markov Chains written by G. George Yin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-30 with Mathematics categories.
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.
Markov Chain Aggregation For Agent Based Models
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Author : Sven Banisch
language : en
Publisher: Springer
Release Date : 2015-12-21
Markov Chain Aggregation For Agent Based Models written by Sven Banisch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-21 with Science categories.
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting “micro-chain” including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the updating rule and governs the dynamics at a Markovian level, plays a crucial part in the analysis of “voter-like” models used in population genetics, evolutionary game theory and social dynamics. The book demonstrates that the problem of aggregation in ABMs - and the lumpability conditions in particular - can be embedded into a more general framework that employs information theory in order to identify different levels and relevant scales in complex dynamical systems