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SUMMARY:Probabilistic computing for Bayesian inference - Vikash K. Mansing
 hka (MIT)
DTSTART:20140402T123000Z
DTEND:20140402T133000Z
UID:TALK51686@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Probabilistic modeling and Bayesian inference provide a unifyi
 ng theoretical framework for uncertain reasoning. They have become central
  tools for engineering machine intelligence\, modeling human cognition\, a
 nd analyzing structured and unstructured data. However\, they often seem f
 ar less unified\, complete and expressive in practice than they are in the
 ory\, and can require significant interdisciplinary expertise to apply. Do
 mains such as robotics and statistics involve diverse modeling idioms\, sp
 eed/accuracy requirements\, dataset sizes\, and approximation techniques. 
 Inference in simple latent variable models can be computationally challeng
 ing\, while state-of-the-art models do not fit within standard formalisms 
 and can be cumbersome to specify\, let alone use.\n\n\nIn this talk\, I wi
 ll describe probabilistic computing systems that address several of these 
 challenges and that fit together into a mathematically coherent software a
 nd hardware stack for Bayesian inference and intelligent computation.\n\nI
  will focus on Venture\, a new\, Turing-complete probabilistic programming
  platform descended from the Church probabilistic programming language. In
  Venture\, models are represented by executable code\, with random choices
  corresponding to latent variables. Inference from data is done via automa
 tic but reprogrammable mechanisms that cover a broad class of approximatio
 n strategies\, including novel hybrids of Markov chain\, sequential Monte 
 Carlo and variational techniques. I will describe applications in text ana
 lysis\, high-dimensional statistics and computer vision that yield a 100x 
 savings in lines of code versus standard approaches. 
LOCATION:Engineering Department\, CBL Room BE-438
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