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
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