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首页> 外文会议>Canadian conference on artificial intelligence >De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency
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De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency

机译：去因果化NAT建模贝叶斯网络以提高推理效率

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Conditional independence encoded in Bayesian networks (BNs) avoids combinatorial explosion on the number of variables. However, BNs are still subject to exponential growth of space and inference time on the number of causes per effect variable in each conditional probability table (CPT). A number of space-efficient local models exist that allow efficient encoding of dependency between an effect and its causes, and can also be exploited for improved inference efficiency. We focus on the Non-Impeding Noisy-AND Tree (NIN-AND Tree or NAT) models due to its multiple merits. In this work, we develop a novel framework, de-causalization of NAT-modeled BNs, by which causal independence in NAT models can be exploited for more efficient inference. We demonstrate its exactness and efficiency impact on inference based on lazy propagation (LP).
机译：贝叶斯网络（BNs）中编码的条件独立性避免了变量数量的组合爆炸式增长。但是，BN仍然要根据每个条件概率表（CPT）中每个效应变量的起因数，对空间和推理时间进行指数增长。存在许多节省空间的局部模型，这些局部模型允许对效果及其原因之间的依赖关系进行高效编码，并且还可以利用这些模型来提高推理效率。由于其多种优点，我们专注于非隐含噪声与树（NIN-AND Tree或NAT）模型。在这项工作中，我们开发了一个新颖的框架，即对NAT建模的BN进行去因化处理，通过该框架，可以利用NAT模型中的因果独立性进行更有效的推理。我们证明了其准确性和效率对基于惰性传播（LP）的推理的影响。

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