Title
Inferring Covariances For Probabilistic Programs
Abstract
We study weakest precondition reasoning about the (co) variance of outcomes and the variance of run-times of probabilistic programs with conditioning. For outcomes, we show that approximating (co) variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co) variances is Sigma(0)(2)-complete. As a consequence, neither lower nor upper bounds are computably enumerable. We therefore present invariant-based techniques that do enable enumeration of both upper and lower bounds, once appropriate invariants are found. Finally, we extend this approach to reasoning about run-time variances.
Year
DOI
Venue
2016
10.1007/978-3-319-43425-4_14
QUANTITATIVE EVALUATION OF SYSTEMS, QEST 2016
Keywords
DocType
Volume
Probabilistic programs, Covariance, Run-time
Conference
9826
ISSN
Citations 
PageRank 
0302-9743
4
0.40
References 
Authors
4
3
Name
Order
Citations
PageRank
Benjamin Lucien Kaminski112610.46
Joost-Pieter Katoen24444289.65
Christoph Matheja3684.92