Guaranteed bounds for posterior inference in universal probabilistic programming.
Raven BeutnerC.-H. Luke OngFabian ZaiserPublished in: PLDI (2022)
Keyphrases
- posterior probability
- bayesian networks
- probabilistic model
- belief networks
- inference process
- probabilistic reasoning
- lower bound
- upper bound
- markov chain monte carlo methods
- bayesian inference
- probability distribution
- programming language
- logical inference
- probabilistic networks
- markov chain monte carlo
- variable elimination
- bayes nets
- lower and upper bounds
- programming environment
- factor graphs
- upper and lower bounds
- bayesian model
- probabilistic logic
- probabilistic inference
- inference mechanism
- probabilistic modeling
- generative model
- object oriented
- independence assumption
- posterior distribution
- first order logic
- marginal probabilities
- graphical models
- bayesian reasoning
- optimal solution