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 used to solve Markov
models. Providing a unified treatment of Markov chains and Markov
decision processes in a single volume, Markov Chains and Decision
Processes for Engineers and Managers supplies a highly detailed
description of the construction and solution of Markov models that
facilitates their application to diverse processes.
Organized around Markov chain structure, the book begins with
descriptions of Markov chain states, transitions, structure, and
models, and then discusses steady state distributions and passage
to a target state in a regular Markov chain. The author treats
canonical forms and passage to target states or to classes of
target states for reducible Markov chains. He adds an economic
dimension by associating rewards with states, thereby linking a
Markov chain to a Markov decision process, and then adds decisions
to create a Markov decision process, enabling an analyst to choose
among alternative Markov chains with rewards so as to maximize
expected rewards. An introduction to state reduction and hidden
Markov chains rounds out the coverage.
In a presentation that balances algorithms and applications, the
author provides explanations of the logical relationships that
underpin the formulas or algorithms through informal derivations,
and devotes considerable attention to the construction of Markov
models. He constructs simplified Markov models for a wide
assortment of processes such as the weather, gambling, diffusion of
gases, a waiting line, inventory, component replacement, machine
maintenance, selling a stock, a charge account, a career path,
patient flow in a hospital, marketing, and a production line. This
treatment helps you harness the power of Markov modeling and apply
it to your organization s processes.
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