Table of contents
Bayesian Networks in Philosophy (PHI420/PHI555, 3 credits, Spring 2021) is an exploration of the philosophical applications of Bayesian networks (perhpas more aptly called, probabilistic graphical models). The course consists of three parts (see details below):
Marcello Di Bello – email: mdibello AT asu DOT edu
Class meets every Monday, 3:00-5:45 PM
At the completion of the course, you will have:
You will be expected to:
You are encouraged to pursue your own interests and lines of research. Emphasis is on conceptual understanding, not computations.
All assignments must be submitted in Canvas by the due date. Please check Canvas for details.
Your final grade will be the weighted average of the letter grades you received on individual assignments. You must pass the probability exam in order to pass the course.
(most videos below are by John Tsitsiklis from MIT)
(most videos below are by John Tsitsiklis from MIT)
(videos below are by Adnan Darwiche from UCLA)
The videos above are based on Chapter 4 of Darwiche’s book, Modeling and Reasoning with Bayesian Networks.
This lecture is about the problem of bare statistical evidence (e.g. Smith 2017 below) as an objection to the claim put forward by legal probabilists that thresholds of legal proof can be represented by thresholds of evidential probability. Cases of bare statistical evidence should be understood as instances of Hidden Causal Markov Models. Once we understand them as such, it becomes clear that bare statistical evidence in general does not justify an assignment of probability above the relevant thresholds (or if it justifies it, then legal proof is established). In this way, legal probabilist can avoid the problem of bare statistical evidence.
Preparatory readings:
Preparatory readings:
Note the change of time: the talk by Stephan Hartman starts at 9 AM (AZ time) and will end at 10:15 AM. Class will reconvene at 3PM and end at 4PM for a discussion among ourselves about the talk.
Preparatory readings:
Dawid, Hartmann and Sprenger, The No Alternatives Argument
Hartmann, Bayes Nets and Rationality
Dardashti and Hartmann, Assessing Scientific Theories: The Bayesian Approach
Extra readings:
Hartman and Sprenger, Bayesian Philosophy Of Science
Hartman, Theory reduction
Preparatory readings:
Richard Scheines, An Introduction to Causal Inference
Jonathan Herington, Measuring Fairness in an Unfair World
Note the change of time: the talk by Jan Sprenger starts at 10 AM (AZ time) and will end at 11:15 AM. Class will reconvene at 3PM and end at 4PM for a discussion among ourselves about the talk.
Preparatory readings:
Student presentations
Other topics: One topic we did not cover for lack of time was the application of Bayesian network to political philosophy. See for rexample this paper by Luc Bovens and Claus Beisbart, Measuring voting power for dependent voters through causal models
The syllabus is a statement of intent and serves as an implicit agreement between the instructor and the student. Every effort will be made to avoid changing the course schedule but the possibility exists that unforeseen events will make syllabus changes necessary. Remember to check your ASU email and the course site often.
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