Jan Sprenger is Professor of Philosophy of Science at the University of Turin. After completing an undergraduate degree in mathematics, he obtained his PhD in Philosophy at the University of Bonn in 2008. He then took up a post at Tilburg University, first working as Assistant Professor (2008-13) and subsequently as Full Professor (2014-17). He also directed the Tilburg Center for Logic, Ethics and Philosophy of Science (TiLPS). Sprenger's research and publications span a wide range of topics, mainly in philosophy of science and uncertain reasoning, but also in logic, group decision-making, and empirical work on human cognition.Stephan Hartmann is Professor of Philosophy of Science at LMU Munich, Alexander von Humboldt Professor, and Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Between 2007 and 2012 he worked at Tilburg University, where he was Chair in Epistemology and Philosophy of Science and Director of the Tilburg Center for Logic and Philosophy of Science (TiLPS). Prior to this, he was Professor of Philosophy at the London School of Economics and Director of its Centre for Philosophy of Natural and Social Science. He was President of the European Philosophy of Science Association (2013-17) and President of the European Society for Analytic Philosophy (2014-17). Hartmann's primary research and teaching areas are philosophy of science, philosophy of physics, formal epistemology, and social epistemology. His current interests also include the philosophy and psychology of reasoning and argumentation.

For anyone with a serious interest in formal methods in the philosophy of science, this book is essential reading.

• 1: Theme: Bayesian Philosophy of Science 2: Variation 1: Confirmation and Induction 3: Variation 2: The No Alternatives Argument 4: Variation 3: Scientific Realism and the No Miracles Argument 5: Variation 4: Learning Conditional Evidence 6: Variation 5: The Problem of Old Evidence 7: Variation 6: Causal Strength 8: Variation 7: Explanatory Power 9: Variation 8: Intertheoretic Reduction 10: Variation 9: Hypothesis Testing and Corroboration 11: Variation 10: Simplicity and Model Selection 12: Variation 11: Scientific Objectivity 13: Variation 12: Models, Idealizations and Objective Chance Conclusion: The Theme Revisited

Jan Sprenger is Professor of Philosophy of Science at the University of Turin. After completing an undergraduate degree in mathematics, he obtained his PhD in Philosophy at the University of Bonn in 2008. He then took up a post at Tilburg University, first working as Assistant Professor (2008-13) and subsequently as Full Professor (2014-17). He also directed the Tilburg Center for Logic, Ethics and Philosophy of Science (TiLPS). Sprenger's research and publications span a wide range of topics, mainly in philosophy of science and uncertain reasoning, but also in logic, group decision-making, and empirical work on human cognition.Stephan Hartmann is Professor of Philosophy of Science at LMU Munich, Alexander von Humboldt Professor, and Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Between 2007 and 2012 he worked at Tilburg University, where he was Chair in Epistemology and Philosophy of Science and Director of the Tilburg Center for Logic and Philosophy of Science (TiLPS). Prior to this, he was Professor of Philosophy at the London School of Economics and Director of its Centre for Philosophy of Natural and Social Science. He was President of the European Philosophy of Science Association (2013-17) and President of the European Society for Analytic Philosophy (2014-17). Hartmann's primary research and teaching areas are philosophy of science, philosophy of physics, formal epistemology, and social epistemology. His current interests also include the philosophy and psychology of reasoning and argumentation.

For anyone with a serious interest in formal methods in the philosophy of science, this book is essential reading.

• 1: Theme: Bayesian Philosophy of Science 2: Variation 1: Confirmation and Induction 3: Variation 2: The No Alternatives Argument 4: Variation 3: Scientific Realism and the No Miracles Argument 5: Variation 4: Learning Conditional Evidence 6: Variation 5: The Problem of Old Evidence 7: Variation 6: Causal Strength 8: Variation 7: Explanatory Power 9: Variation 8: Intertheoretic Reduction 10: Variation 9: Hypothesis Testing and Corroboration 11: Variation 10: Simplicity and Model Selection 12: Variation 11: Scientific Objectivity 13: Variation 12: Models, Idealizations and Objective Chance Conclusion: The Theme Revisited