论文标题
贝叶斯网络中潜在混杂因素的发现和密度估计有证据
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
论文作者
论文摘要
发现和参数化的潜在混杂因素分别代表了因果结构学习和密度估计中的重要和具有挑战性的问题。在本文中，我们专注于发现和学习潜在混杂因素的分布。此任务需要来自统计和机器学习不同领域的解决方案。我们结合了各种贝叶斯方法的要素，期望最大化，攀爬搜索和结构学习在因果关系不足的假设下学习。我们提出了两种学习策略。一种可以最大化模型选择准确性，另一种可以提高计算效率，以换取精确度的较小降低。前一种策略适用于小型网络，后者适用于中等大小的网络。相对于现有解决方案，两种学习策略都表现良好。
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learning. We combine elements of variational Bayesian methods, expectation-maximisation, hill-climbing search, and structure learning under the assumption of causal insufficiency. We propose two learning strategies; one that maximises model selection accuracy, and another that improves computational efficiency in exchange for minor reductions in accuracy. The former strategy is suitable for small networks and the latter for moderate size networks. Both learning strategies perform well relative to existing solutions.
