Paper Info
Title
Compositional Stochastic Modeling and Probabilistic Programming
Abstract
Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in parallel (and possibly interacting) have summed time-evolution operators. From this foundation, algorithms for simulation, inference and model reduction may be systematically derived. The useful consequences are potentially far-reaching in computational science, machine learning and beyond. Hybrid compositional stochastic modeling/probabilistic programming approaches may also be possible.
Year
Venue
Field
2012
CoRR
Computer science,Inference,Theoretical computer science,Artificial intelligence,Operator (computer programming),Probabilistic logic,Machine learning,Semantics
DocType
Volume
Citations 
Journal
abs/1212.0582
0
PageRank 
References 
Authors
0.34
9
1
Name
Order
Citations
PageRank
Eric Mjolsness11058140.00