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SUMMARY:A Tutorial on Probabilistic Programming - Prof. Frank Wood (Oxford
 )
DTSTART:20141023T130000Z
DTEND:20141023T160000Z
UID:TALK54300@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:In probabilistic programming generative processes are represen
 ted via computer programs with internal random choices.  Observed data con
 dition execution paths of probabilistic programs.  Running a probabilistic
  program characterizes the posterior distribution of internal random choic
 es and memory state (execution paths) arising from program executions that
  could have generated the observations.  This subsumes Bayesian inference 
 in probabilistic models and so it could be claimed that probabilistic prog
 ramming is the natural generalization of Bayesian probabilistic modeling.\
 n\nProbabilistic programming has the potential to greatly reduce the techn
 ical and cognitive overhead for writing and designing novel probability mo
 dels in all quantitative fields.  In most probabilistic programming system
 s\, including those that will be introduced in this tutorial (Anglican and
  Probabilistic-C)\, inference typically is both decoupled from modeling an
 d fully-automated: probabilistic programs (models) can be written without 
 having to derive and program custom inference algorithms.  The space of mo
 dels specifiable as probabilistic programs is large\, allowing probabilist
 ic programming practitioners to easily write richly expressive models that
  would otherwise be difficult to even mathematically or graphically denote
 .\n
LOCATION:Engineering Department\, CBL Room BE-438
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