Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages.
Daniel LundénJohannes BorgströmDavid BromanPublished in: ESOP (2021)
Keyphrases
- programming language
- sequential monte carlo
- bayesian networks
- object oriented
- general purpose
- strongly typed
- inference process
- belief networks
- high level
- particle filter
- programming environment
- software engineering
- lambda calculus
- logic programming
- visual tracking
- specification language
- denotational semantics
- particle filtering
- database languages
- probabilistic model
- probabilistic inference
- bayesian inference
- graphical models
- posterior probability
- importance sampling
- conditional probabilities
- probability distribution
- pairwise
- image segmentation