Name
Abstract | ||
|---|---|---|
Normalisation in probability theory turns a subdistribution into a proper distribution. It is a partial operation, since it is unde fined for the zero subdistribution. This partiality makes it hard to reason equationally about normalisation. A novel description of normalisation is given as a mathematically well-behaved total function. The output of this 'hyper' normalisation operation is a distribution of distributions. It improves reasoning about normalisation. After developing the basics of this theory of (hyper) normalisation, it is put to use in a similarly new description of conditioning, producing a distribution of conditional distributions. This is used to give a clean abstract reformulation of refinement in quantitative information flow. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.23638/LMCS-13(3:17)2017 | LOGICAL METHODS IN COMPUTER SCIENCE |
DocType | Volume | Issue |
Journal | 13 | 3 |
ISSN | Citations | PageRank |
1860-5974 | 2 | 0.38 |
References | Authors | |
11 | 1 | |