Name
Abstract | ||
|---|---|---|
The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability - via standard Bore! spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several examples. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1017/S0960129518000488 | MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
Conditional probability,disintegration,Bayesian inversion,string diagram,monoidal category | Applied mathematics,Data mining,Bayesian inversion,Conditional probability,Computer science,Communication channel | Journal |
Volume | Issue | ISSN |
29 | 7 | 0960-1295 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenta Cho | 1 | 26 | 3.10 |
| Bart Jacobs 0002 | 2 | 22 | 6.28 |