Markov Chains and Decision Processes for Engineers and Managers
Theodore J. Writing yet another book about Markov chains and Markov decision processes needs — without doubt — some justification. The author of the present textbook reveals his motives in the preface: most books on these topics are highly theoretical or, else, merely provide algorithms to solve particular problems, but without explaining the intuition behind the single steps of these algorithms.
This book was written with the explicit intention to embrace a bit of both ends of the spectrum.
The author introduces the basics of Markov chains, Markov chains with rewards and Markov decision processes for finite state spaces. Keep this in mind when you come across the statement that all irreducible Markov chains are recurrent!
Markov Chains and Decision Processes for Engineers and Managers by Theodore J. Sheskin - PDF Drive
Many standard quantities around these processes are discussed, such as stationary distributions, properties of first passage times, and expected average rewards. There is also a section on state reduction techniques and hidden Markov chains. Instead, the author often justifies a general formula by doing the corresponding calculations with quite concrete Markov chains, either considering just a few states or assuming a simplifying structure in the transition probability matrix.
With this approach, the book does, on one hand, provide the practitioner engineer and managers with concrete formulae to tackle specific questions involving Markov chains. On the other hand, the author tries to give the reader some insight into how these results are derived — in this case, as non-mathematicians are targeted, by means of solid heuristics, rather than mathematical proofs.
knowledgebrief.vvinners.com/sapaq-reiki-madrid.php To develop a solid piece of theory of course not too technical, please , clearly explained and well motivated, exemplified with many, many interesting applications? As for every textbook, the author has to find the difficult balance between all these objectives, which, at the end of the day, is probably a matter of taste.
We show that the problem can be formulated as a Markov Chain under a reasonable set of assumptions.
The states represent the quantized weight of a participant. The transitions between the states represent nutrition and exercise actions. A policy computed using this model represents an intervention strategy for a participant. Given the participant's initial weight and target weight, we show that the computed policy is sensitive to the reward functions that are associated with the actions.
In the future, such an approach can be used to offer wellness interventions to participants. Article :. Need Help?
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