Teacher: Stephan Hartmann

Content: This course aims to show how Bayesian methods can be used to answer central questions in the philosophy of science. To this end, in the first part of the course, students will learn to construct Bayesian models (in particular using the theory of Bayesian networks) and apply them to selected problems. To this end, there will be two tutorial sessions in which students can train their mathematical problem-solving skills. In the second part, we will first briefly talk about different epistemic theories of epistemic justification and then focus on the debate on probabilistic measures of coherence discussed in formal epistemology. We will then examine the possibilities of developing a coherentist Bayesian philosophy of science, focusing in particular on the extent to which this approach can shed light on current debates about scientific explanation and intertheoretical relations. Finally, we will discuss the (possible) limits of Bayesianism and coherentism.

To prepare for the course, students might want to consult the following two books:

• Bovens L. & Hartmann S.: Bayesian Epistemology. Oxford: Oxford University Press, 2003.
• Sprenger J. & Hartmann S.: Bayesian Philosophy of Science. Oxford: Oxford University Press, 2019.