This study addresses the predictive limitation of probabilistic circuits and introduces transformations as a remedy to overcome it. We demonstrate this limitation in robotic scenarios. We motivate that independent component analysis is a sound tool to preserve the independence properties of probabilistic circuits. Our approach is an extension of joint probability trees, which are model-free deterministic circuits. By doing so, it is demonstrated that the proposed approach is able to achieve higher likelihoods while using fewer parameters compared to the joint probability trees on seven benchmark data sets as well as on real robot data. Furthermore, we discuss how to integrate transformations into tree-based learning routines. Finally, we argue that exact inference with transformed quantile parameterized distributions is not tractable. However, our approach allows for efficient sampling and approximate inference.

通过引入转换作为一种解决方法，本研究探讨了概率电路的预测局限性，并展示了其在机器人场景中的局限性。我们提出独立成分分析作为保持概率电路独立性特性的有效工具，扩展了无模型确定性电路的联合概率树方法，实验证明我们的方法在七个基准数据集以及真实机器人数据上使用更少的参数能够实现更高的似然度，并讨论了如何将转换集成到基于树的学习例程中。最后，我们认为使用转换后的分位参数化分布进行准确推断不可行，但是我们的方法可以进行高效的采样和近似推断。

概率电路中的变换集成