Titlebook: Optimized Response-Adaptive Clinical Trials; Sequential Treatment Thomas Ondra Book 2015 Springer Fachmedien Wiesbaden 2015 Allocation Sequ

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Infinite Horizon Markov Decision Problems,for proving the optimality of so called stationary policies. Then we take a look at two important algorithms which solve infinite Markov decision problems: Value Iteration and Policy Iteration. In this chapter we follow the book of [Put94]. Furthermore we use the books [Whi93] and [BR11].

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t decade there has been a great revival of interest in semiclassical methods for obtaining approximate solutions to the Schr?dinger equation. Among them, the WKB approximation and its generalization have attracted much attention to many authors since this method is proven to be useful in obtaining a

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Introduction to Markov Decision Problems,, which provides an appropriate framework for comparing the value of two policies. Finally, to get familiar with the matter, we give some examples of Markov decision problems: we analyse one period Markov decision problems, discuss a card game, and we explain how a single product stochastic inventor

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Infinite Horizon Markov Decision Problems,for proving the optimality of so called stationary policies. Then we take a look at two important algorithms which solve infinite Markov decision problems: Value Iteration and Policy Iteration. In this chapter we follow the book of [Put94]. Furthermore we use the books [Whi93] and [BR11].

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Markov Decision Problems and Clinical Trials,e future trial members already benefit from the previous ones. The goal is to identify the better treatment and keep the number of trial members treated with the inferior therapy small. In [BE95] and [HS91] we find an approach using Bandit models which are similar to Markov decision problems. In [Pr