Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards
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Markovian Decision Processes With Uncertain Transition Probabilities Or Rewards
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Author :
language : en
Publisher:
Release Date : 1959
Markovian Decision Processes With Uncertain Transition Probabilities Or Rewards written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1959 with categories.
Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards
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Author : Edward Allan Silver
language : en
Publisher:
Release Date : 1963
Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards written by Edward Allan Silver and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1963 with Markov processes categories.
In most Markov process studies to date it has been assumed that both the transition prob abilities and rewards are known exactly. The primary purpose of this thesis is to study the effects of relaxing these assumptions to allow more realistic models of real world situations. The Bayesian approach used leads to statistical decision frameworks for Markov processes. The first section is concerned with situations where the transition probabilities are not known ex actly. One approach used incorporates the con cept of multi-matrix Markov processes, processes where it is assumed that one of several known transition matrices is being utilized, but we only have a probability vector on the various matrices rather than knowing exactly which one is governing the process. The second approach assumes more directly that the transition prob abilities themselves are random variables. It is shown that the multidimensional Beta distri bution is a most convenient distribution (for Bayes calculations) to place over the prob abilities of a single row of the transition matrix. Several important properties of the distribution are displayed. Then a method is suggested for determining the multidimensional Beta prior distributions to use for any parti cular Markov process. (Author).
Markov Chains And Decision Processes For Engineers And Managers
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Author : Theodore J. Sheskin
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Markov Chains And Decision Processes For Engineers And Managers written by Theodore J. Sheskin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms u
Markovian Decision Processes With Uncertain Transition Probabilities
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Author : John M. Cozzolino
language : en
Publisher:
Release Date : 1965
Markovian Decision Processes With Uncertain Transition Probabilities written by John M. Cozzolino and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with categories.
A dynamic programming formulation for the Markovian decision process when transition probabilities are unknown is proposed. This formulation is used to solve simple problems, but is shown to be too difficult to apply to more complex systems. Various approximate methods are then proposed and discussed. A simple approximating algorithm is finally presented. (Author).
Markov Decision Processes In Artificial Intelligence
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Author : Olivier Sigaud
language : en
Publisher: John Wiley & Sons
Release Date : 2013-03-04
Markov Decision Processes In Artificial Intelligence written by Olivier Sigaud 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 2013-03-04 with Technology & Engineering categories.
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
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.
Markovian Decision Processes With Uncertain Transition Matrices Or And Probabilistic Observation Of States
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Author : Jayantilal K. Satia
language : en
Publisher:
Release Date : 1968
Markovian Decision Processes With Uncertain Transition Matrices Or And Probabilistic Observation Of States written by Jayantilal K. Satia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1968 with Markov processes categories.
Simulation Based Optimization Of Markov Decision Processes
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Author : Peter Marbach
language : en
Publisher:
Release Date : 1998
Simulation Based Optimization Of Markov Decision Processes written by Peter Marbach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Markov Decision Processes With Applications To Finance
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Author : Nicole Bäuerle
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-06
Markov Decision Processes With Applications To Finance written by Nicole Bäuerle 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 2011-06-06 with Mathematics categories.
The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations research. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision processes and stopping problems. The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers in both applied probability and finance, and provides exercises (without solutions).
Simulation Based Algorithms For Markov Decision Processes
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Author : Hyeong Soo Chang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-26
Simulation Based Algorithms For Markov Decision Processes written by Hyeong Soo Chang 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 2013-02-26 with Technology & Engineering categories.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.