Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure.

贝叶斯网络的因子图简化范式是实现推理图的强大范例。不幸的是，即使对于相对较小的网络，这些网络的计算和存储成本也可能相当可观，这是在实践中经常未充分使用这些结构的主要原因之一。在这项工作中，通过详细的算法和结构分析，提出了各种降低成本的解决方案。而且，还分析了经典批处理学习算法的在线版本，在无人监督的情况下显示出非常相似的结果，但性能要好得多。如果要构建多层结构，这可能是必不可少的。所提出的解决方案以及可能的在线学习算法都包含在一个非常高效的C ++库中，特别是与直接使用众所周知的和积和最大似然算法相比时。将特别参考潜在变量模型结构来讨论获得的结果。