A Tutorial on Probabilistic Programming

Add to your list(s) Download to your calendar using vCal

• Prof. Frank Wood (Oxford)
• Thursday 23 October 2014, 14:00-17:00
• Engineering Department, CBL Room BE-438.

If you have a question about this talk, please contact Zoubin Ghahramani.

In probabilistic programming generative processes are represented via computer programs with internal random choices. Observed data condition execution paths of probabilistic programs. Running a probabilistic program characterizes the posterior distribution of internal random choices 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 programming is the natural generalization of Bayesian probabilistic modeling.

Probabilistic programming has the potential to greatly reduce the technical and cognitive overhead for writing and designing novel probability models in all quantitative fields. In most probabilistic programming systems, including those that will be introduced in this tutorial (Anglican and Probabilistic-C), inference typically is both decoupled from modeling and fully-automated: probabilistic programs (models) can be written without having to derive and program custom inference algorithms. The space of models specifiable as probabilistic programs is large, allowing probabilistic programming practitioners to easily write richly expressive models that would otherwise be difficult to even mathematically or graphically denote.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

• Seminar
• All Talks (aka the CURE list)
• Biology
• CBL important
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge talks
• Chris Davis' list
• Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
• Engineering Department, CBL Room BE-438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ML
• Machine Learning @ CUED
• Machine Learning Summary
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• Trust & Technology Initiative - interesting events
• bld31
• dh539
• kt532's list
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.