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  \textbf{\textsc{STANFORD BERKELEY JOINT COLLOQUIUM}}\\[5pt]

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  4:00 p.m., Tuesday, November 7, 2006\\
  Evans Hall Room 60\\
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  \textsl{Wing H. Wong} \\
Department of Statistics\\
Stanford University\\
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  \textbf{Learning causal Bayesian network structures}
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\noindent  We propose a method for the computational inference of
directed acyclic graphical structures given data from experimental
interventions. Order-space MCMC, equi-energy sampling, importance weighting and
stream-based computation are combined to create a fast algorithm for
learning causal Bayesian network structures. (This is based on joint work
with Byron Ellis.)

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