The Solution of a Generalization of a Bayesian Stopping Problem of MacKinnon

Karl Hinderer and Michael Stieglitz
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Karl Hinderer: Universität Karlsruhe
Michael Stieglitz: Universität Karlsruhe

A chapter in Perspectives on Operations Research, 2006, pp 69-92 from Springer

Abstract: Abstract 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.

Keywords: Bayesian control models; Bayesian optimal stopping (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-8350-9064-4_5

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DOI: 10.1007/978-3-8350-9064-4_5

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