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Tuesday
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This course introduces selected topics in Bayesian epistemology. Bayesian epistemology provides formal models of credence and discusses how to rationally form, organize, and update credences in light of evidence. To offer a partial overview of this fast-growing research field, our course encompasses foundational, challenging, and practical problems. The first part of the course addresses various synchronic and diachronic rationality norms for credence, such as probabilism, the principle of indifference, and principles of deference. The second part deals with some of the most discussed challenges, such as the sleeping beauty problem, the old evidence problem, uncertain learning, and modelling the weights of evidence. The final part explores how Bayesian epistemology can be applied to everyday and scientific reasoning, including inductive (confirmation), abductive (inference to the best explanation), and causal reasoning (causal Bayesian networks). A basic knowledge of first-order logic is presupposed, and familiarity with probability calculus and set-theoretical reasoning is welcome. The course is accompanied by an exercise unit where weekly exercises are discussed.