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THEMATIC PROGRAMS |
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| May 12, 2024 |
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Causal Interpretation and Identification of Conditional Independence Structures Seminar 3 Schedule: Learning Causal Models
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| Tuesday, November 2 | Wednesday, November 3 | Thursday, November 4 | Friday, November 5 |
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| 9:00 - 9:30 p.m. REGISTRATION - 2nd Floor, Fields Institute 9:30 - 11:00 p.m. David Heckerman Decision-Theoretic Foundations for Causal Reasoning 2:00 - 4:00 p.m. COXETER LECTURE SERIES Greg Cooper Causal Discovery from a Mixture of Experimental and Observational Data |
9:00 - 11:00 a.m. Paolo Guidici MCMC Methods for Structural Learning in Graphical Models 2:00 - 4:00 p.m. Steffen Lauritzen Perfect Simulation for Model Averaging | 9:00 - 10:00 a.m. Thomas Richardson Causal Inference from Observational Data via Conditional Independence 10:15 - 11:00 a.m. David Heckerman A Bayesian Approach to Learning Causal Networks 2:00 - 3:00 Jie Cheng 3:30 - 5:30 p.m. Glenn Shafer SEWALL WRIGHT LECTURE The Language of Causality |
9:00 - 10:00 p.m. Johan Andersen The EM Algorithm for Bayesian Networks with Mixed Discrete-Gaussian Variables 10:00 - 11:00 p.m. Steffen Lauritzen The ME Algorithm for Maximizing a Conditional Likelihood Function 11:15 - 12:30 p.m. Brendan Frey Inference and Learning in Bayesian Networks 12:30 - 1:30 p.m. David Heckerman (Room 230) Causal Discovery from Non-Experimental Data 3:30 - 5:30 p.m. Brendan Frey Iterative Decoding of Error-Correcting Codes (turbo-decoding) |
| Monday, November 8 | 9:00 - 11:00 Thomas Richardson A Comparison of MAG Models, Summary Graphs and MC Graphs |
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