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Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation
Published on May 06, 20116423 Views
We describe a general method of transforming arbitrary programming languages into probabilistic programming languages with straightforward MCMC inference engines. Random choices in the program are
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Chapter list
Lightweight Implementations of Probabilistic Programming Languages via Transformational Compilation00:00
Implementing Probabilistic Programming LanguagesWithout the Agonizing Pain00:14
Summary00:20
Outline01:17
Probabilistic Programming01:34
The Big Idea01:42
Distributions over Traces02:21
Example: LDA03:34
Nonparametrics04:06
Example: ((H)DP)MM05:20
Meta-Modeling07:03
Lightweight PPL Implementations07:38
Inference07:43
Observation: Execution Trace (1)08:09
Observation: Execution Trace (2)09:18
Transformational Compilation09:38
MCMC over Execution Traces10:09
But What Name? (1)11:21
But What Name? (2)12:31
But What Name? (3)12:53
Generating Names13:44
Example: Geometric15:19
Minimal Interpretative Overhead15:58
New Inference Options17:25
Different Inference Options17:39
Dynamic Dependency Analysis (1)19:08
Dynamic Dependency Analysis (2)20:11
Dynamic Dependency Analysis (3)20:31
Example: mesh inference21:18
Summary22:41
Thank you!23:31