Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference.
Daniel LundénJoey ÖhmanJan KudlickaViktor SenderovFredrik RonquistDavid BromanPublished in: ESOP (2022)
Keyphrases
- programming language
- sequential monte carlo
- object oriented
- general purpose
- bayesian networks
- programming environment
- high level
- high level programming language
- strongly typed
- denotational semantics
- software engineering
- lambda calculus
- database languages
- probabilistic inference
- probabilistic model
- spatio temporal
- databases
- particle filter
- generative model
- bayesian inference
- abstract data types