Back to Results
First PageMeta Content
Monte Carlo methods / Computational statistics / Statistical models / Markov chain Monte Carlo / Bayesian inference / Gibbs sampling / Normal distribution / Posterior probability / Kullback–Leibler divergence / Statistics / Bayesian statistics / Statistical theory


When are probabilistic programs probably computationally tractable? Cameron E. Freer Univ. of Hawai‘i at M¯anoa
Add to Reading List

Document Date: 2011-01-31 01:40:47

Open Document

File Size: 548,45 KB

Share Result on Facebook

City

Cambridge / Amsterdam / /

Company

Neural Information Processing Systems / Infer.NET / Navia Systems / Microsoft / Vikash K. Mansinghka Navia Systems / /

Country

Netherlands / /

Currency

pence / /

/

Facility

Massachusetts Institute of Technology / /

IndustryTerm

probabilistic programming systems / randomized algorithm / inference algorithms / probabilistic tool / purpose tools / naive single-site / linear systems / energy / systematic stochastic search / /

Organization

National Science Foundation / MIT / Massachusetts Institute of Technology / IEEE Computer Society / Cameron E. Freer Univ. of Hawai‘i / /

Person

Ruslan Salakhutdinov / Isaac Chuang / Stuart Russell / Josh Tenenbaum / Jonathan Kelner / John Langford / David Wingate / Manfred Opper / Ross Lippert / Daniel M. Roy / N. D. Goodman / V / Keith Bonawitz / Cameron E. Freer Univ / /

Position

driver / graphical model / etc / /

ProvinceOrState

Montana / Massachusetts / /

Technology

functional programming / cryptography / inference algorithms / simulation / universal MCMC algorithms / MCMC algorithms / Typical Gibbs algorithms / /

URL

http /

SocialTag