I plan to having a significant focus on #Bayesianism in my advanced #philsci course, next term. How much time should I plan to devote to explaining the basics, assuming that most students lack any background in probability and no math skills?
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In my experience, the basics of Bayesianism—solution to Hempel's tacking paradox; Dutch Book Argument only demands coherence; conditionalization and Bayes' Theorem; confirmation goes with "likelihood"; problem of old evidence—itself serves as an great introduction to the probability maths needed.
This probably means that you have to spend quite a bit of time on the basics while doing so. So I’m wondering how I should plan the semester (I have ~10x1.5 hours lectures).
If you want a slow, relatively easy coverage of the basics, there's Hacking's _An Introduction to Probability and Inductive Logic_. But going through it chapter by chapter, discussing concepts and exercises in class, etc., takes a long time. If your students can go at a faster pace, that's better.
I hope to rely heavily on the these books:
Colin Howson & Peter Urbach, Scientific Reasoning. The Bayesian Approach.
Stephan Hartmann & Jan Sprenger, Bayesian Philosophy Of Science.
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having some intuition for conditional probabilities is a prereq, and i think that's a large ask.
i think Monty Hall problem is a fun motivater.
#Bayesianism #philsky
Colin Howson & Peter Urbach, Scientific Reasoning. The Bayesian Approach.
Stephan Hartmann & Jan Sprenger, Bayesian Philosophy Of Science.