Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming.
Raven BeutnerLuke OngFabian ZaiserPublished in: CoRR (2022)
Keyphrases
- posterior probability
- bayesian networks
- probabilistic model
- inference process
- belief networks
- markov chain monte carlo methods
- upper bound
- markov chain monte carlo
- probabilistic reasoning
- probabilistic inference
- logical inference
- generative model
- lower bound
- posterior distribution
- bayesian reasoning
- bayesian inference
- programming language
- bayesian framework
- probability distribution
- marginal probabilities
- factor graphs
- loopy belief propagation
- programming environment
- probabilistic logic
- independence assumption
- worst case
- upper and lower bounds
- statistical relational learning
- variable elimination
- uncertain data
- probabilistic networks
- dynamic bayesian networks
- inference mechanism
- probabilistic modeling
- turing machine
- error bounds
- semi supervised
- gaussian process