# Fine-Tuning the Odds in Bayesian Networks

@article{Salmani2021FineTuningTO, title={Fine-Tuning the Odds in Bayesian Networks}, author={Bahar Salmani and Joost-Pieter Katoen}, journal={ArXiv}, year={2021}, volume={abs/2105.14371} }

. This paper proposes new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables. The key idea is to exploit scalable and powerful techniques for synthesis problems in parametric Markov chains. Our techniques are applicable to arbitrarily many, possibly dependent, parameters that may occur in multiple CPTs. This lifts the severe restrictions on parameters, e.g., by restricting the number of parametrized CPTs to one or two, or by…

## 3 Citations

### Parameter Synthesis in Markov Models: A Gentle Survey

- Computer ScienceArXiv
- 2022

The main ideas underlying state-of-the-art algorithms that established an impressive leap over the last decade enabling the fully automated analysis of models with millions of states and thousands of parameters are described.

### Abstraction-Refinement for Hierarchical Probabilistic Models

- Computer ScienceCAV
- 2022

The key ideas to accelerate analysis of Markov decision processes are to treat the behavior of the subroutine as uncertain and only remove this uncertainty by a detailed analysis if needed, and to abstract similar subroutines into a parametric template, and then analyse this template.

### Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

- Computer ScienceCAV
- 2021

The symbolic verification of Markov chains with respect to finite horizon reachability properties is revisit and Rubicon, a tool that transpiles Prism models to the probabilistic inference tool Dice, is developed as a first step towards integrating both perspectives.

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