Resource type
Online resource

A Provably Correct Sampler for Probabilistic Programs

Resource type
Authors/contributors
Title
A Provably Correct Sampler for Probabilistic Programs
Abstract
We consider the problem of inferring the implicit distribution specified by a probabilistic program. A popular inference technique for probabilistic programs called Markov Chain Monte Carlo or MCMC sampling involves running the program repeatedly and generating sample values by perturbing values produced in “previous runs”. This simulates a Markov chain whose stationary distribution is the distribution specified by the probabilistic program.
Pages
21
Date
2015
Language
en
Library Catalog
Zotero
Extra
ZSCC: 0000017
Citation
Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
PROBABILITY & STATISTICS
Methodology
Processing time: 0.02 seconds

Graph of references

(from Zotero to Gephi via Zotnet with this script)
Graph of references