Talks and Poster Presentations (with Proceedings-Entry):
E. Bartocci, L. Kovacs, S. Stankovic:
"Analysis of Bayesian Networks via Prob-Solvable Loops";
Talk: Proc. of ICTAC 2020: the 17th International Colloquium on Theoretical Aspects of Computing,
Macau S.A.R., China;
- 2020-12-04; in: "Proc. of ICTAC 2020: the 17th International Colloquium on Theoretical Aspects of Computing",
Prob-solvable loops are probabilistic programs with polynomial assignments over random variables and parametrised distributions, for which the full automation of moment-based invariant generation is decidable. In this paper we extend Prob-solvable loops with new features essential for encoding Bayesian networks (BNs). We show that various BNs, such as discrete, Gaussian, conditional linear Gaussian and dynamic BNs, can be naturally encoded as Prob-solvable loops. Thanks to these encodings, we can automatically solve several BN related problems, including exact inference, sensitivity analysis, filtering and computing the expected number of rejecting samples in sampling-based procedures. We evaluate our work on a number of BN benchmarks, using automated invariant generation within Prob-solvable loop analysis.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.