<p>“Bayesian Philosophy of Science” addresses classical topics in philosophy of science, using a single key concept—degrees of beliefs—in order to explain and to elucidate manifold aspects of scientific reasoning. The basic idea is that the value of convincing evidence, good explanations, intertheoretic reduction, and so on, can all be captured by the effect it has on our degrees of belief. This idea is elaborated as a cycle of variations about the theme of representing rational degrees of belief by means of subjective probabilities, and changing them by a particular rule (Bayesian Conditionalization). Partly, the book is committed to the Carnapian tradition of explicating essential concepts in scientific reasoning using Bayesian models (e.g., degree of confirmation, causal strength, explanatory power). Partly, it develops new solutions to old problems such as learning conditional evidence and updating on old evidence, and it models important argument schemes in science such as the No Alternatives Argument, the No Miracles Argument or Inference to the Best Explanation. Finally, it is explained how Bayesian inference in scientific applications—above all, statistics—can be squared with the demands of practitioners and how a subjective school of inference can make claims to scientific objectivity. The book integrates conceptual analysis, formal models, simulations, case studies and empirical findings in an attempt to lead the way for 21th century philosophy of science.</p>