Stochastic Decision Making

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Stochastic Decision making introduces the student to modeling dynamic processes that involve randomness. Any realistic model of a real-world phenomenon must take into account the possibility of randomness. That is, the quantities one is interested in will not be predictable in advance but instead will exhibit an inherent variation that should be taken into account by the model. This is usually accomplished by allowing the model to be probabilistic in nature. Such a model is referred to as a probability model. In this course the following topics, among others, are discussed: basic concepts of probability theory, probability distribution functions, conditional probability, expectation and probability conditioning, Markov chains, Markov decision problems, Poisson processes and continuous time Markov chains. These topics are accompanied by a discussion on their mathematical framework. After completing this course the student will have obtained knowledge of modeling dynamic processes that involve randomness. This includes knowledge about appropriate probability distributions, analysis tools and knowledge of the most relevant and applicable processes. The student will be able to model and analyze all kind of real life practical situations involving stochastic uncertainty.