THEMATIC PROGRAMS

October 24, 2014

Causal Interpretation and Identification of Conditional Independence Structures

Seminar 3 Schedule: Learning Causal Models
November 2 - 5, 1999

Organizing Committee:

David Heckerman, Microsoft Research
Steffen Lauritzen, Aalborg University




Tuesday, November 2 Wednesday, November 3 Thursday, November 4 Friday, November 5
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