Bnlearn是否计算非线性相关性？
我最近开始使用R中的bnlearn软件包来检测数据集中目标节点的马尔可夫毯子。
根据我对贝叶斯推论的理解，如果两者之间存在因果关系，并且使用某些条件独立性测试来测量这两个节点，以检查潜在的混杂因素，以检查是否相关。
我只是想澄清bnlearn检查这些测试中线性和非线性相关性的检查。我尝试在但是我什么也没得到。
如果某人可以解释bnlearnn执行CI测试，那将非常有帮助。
谢谢一堆＆lt; 3
I've recently started using the bnlearn package in R for detecting Markov Blankets of a target node in the dataset.
Based on my understanding of Bayesian Inference, two nodes are connected if there is a causal relationship between the two and this is measured using some conditional independence tests to check for correlation while taking into account potential confounders.
I just wanted to clarify if bnlearn checks for both linear and non-linear correlations in these tests. I tried looking for stuff about this in the documentation for the package but I wasn't able to get anything.
It would be really helpful if someone can explain how bnlearn performs the CI tests.
Thanks a bunch <3
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相关性意味着统计依赖性，但反之亦然。在没有相关性的情况下，例如周期性信号（sin（x）和x之间的相关性在许多时期内非常低）。统计依赖性的概念比相关性更为抽象，因此文档的书写方式不同。
正如您在SIN（X）和X的示例中看到的那样：这确实是一种非线性依赖性，应由贝叶斯网络捕获。
Correlation implies statistical dependence, but not vice versa. There are cases of statistical dependence where there is no correlation, e.g. in periodic signals (correlation between sin(x) and x is very low for many periods). The concept of statistical dependence is more abstract than correlation and thus the documentation is written differently.
As you can see in the example of sin(x) and x: This is indeed a non-linear dependency which should be captured by the Bayesian network.