bayesnetsinphilosophy

Table of contents

Course overview

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):

  1. Probability theory refresher
  2. Intro to Bayesian networks
  3. Philosophical applications

Instructor

Marcello Di Belloemail: mdibello AT asu DOT edu

Class meetings

Class meets every Monday, 3:00-5:45 PM

Learning outcomes

At the completion of the course, you will have:

Coursework and major assignments

You will be expected to:

You are encouraged to pursue your own interests and lines of research. Emphasis is on conceptual understanding, not computations.

Assignment submissions

All assignments must be submitted in Canvas by the due date. Please check Canvas for details.

Final Grade

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.

Course topics and schedule

PART 1: Probability theory refresher

January 11 - Introduction

January 18 - MLK day - no class

January 25 and February 1

(most videos below are by John Tsitsiklis from MIT)

Sample space and probability axioms

Conditional probability and Bayes’s rule

February 8

(most videos below are by John Tsitsiklis from MIT)

Independence

Other important concepts

PART 2: Bayesian networks

February 15

Basic idea

February 22

Confounding and Simpon’s Paradox

March 1

More in depth

(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.

Other introductions to Bayesian networks

Bayesian network software

Benchmark examples of Bayesian networks

PART 3: Philosophical applications

March 15 - Guest speaker: Chad Lee-Stronach, Statistical Evidence and Hidden Markov Models

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:

March 22 - Guest speakers: Alicja Kowalewska and Rafal Urbaniak, Coherence and Bayesian Networks

Preparatory readings:

Mach 29 - Guest speaker: Stephan Hartmann, Bayesian Networks in Philosophy of Science [Slides]

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:

Extra readings:

April 5 - Guest speaker: Jonathan Herington, Causal Models and Algorithmic Fairness

Preparatory readings:

April 12 - Guest speaker: Jan Sprenger, A Probabilistic Theory of Causal Strength

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:

April 19 - Voting

Syllabus Disclaimer

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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