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Published in:
2006 | OriginalPaper | Chapter
The Solution of a Generalization of a Bayesian Stopping Problem of MacKinnon
Authors : Karl Hinderer, Michael Stieglitz
Published in: Perspectives on Operations Research
Publisher: DUV
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We consider a substantial generalization of a problem proposed by MacKinnon (2003). Within the setting of Bayesian Markovian decision processes we derive for the maximal expected
N
-stage reward
d
n
for a random initial state an integral recursion and an algorithmic recursion. From the former we obtain results about the dependence of
d
N
on several parameters while the latter serves the same purpose, but also yields a numerical solution. An optimal policy is given in the form of an optimal stopping time. The model with a random initial state is dealt with by an appropriate choice of the state space.