论文标题
通过符号模型检查的贝叶斯推断
Bayesian Inference by Symbolic Model Checking
论文作者
论文摘要
本文将离散马尔可夫链的概率模型检查技术应用于贝叶斯网络中的推断。我们将简单的翻译从贝叶斯网络转化为树状的马尔可夫链，以便可以将推理降低为计算可及性概率。使用在风暴模型检查器之上的原型实现，我们表明符号数据结构（例如多末端BDDS（MTBDDS））非常有效地推断大型贝叶斯网络基准。我们将我们的结果与使用概率句子决策图和VTREES（AI推论工具中的可扩展符号技术）进行了比较。
This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.
