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As a famous sub-structure of Bayesian network, causal polytree is able to recover the causality very efficiently.
Here, I implement pearl's classical algorithm here for easy using. Details can be seen in Pearl's paper.
To recover general Causal polytree, one can download "Fisher's exact test" in my space for conditional independence test.
One can start from ControlCenter.m, I add a simple...
The probabilistic logic sampling algorithm is described in (Henrion 1988). Here is the website:
About the theory under PLS, please refer...
Our basic idea is based on (n,k)-gray code which was introduced in one paper named :"Generalized Gray Codes with Applications".
Our extention is allowing each digit ranged from different digit which is widely useful in some...
A simple, fast and short code for beginners, who cares about Fisher's Exact Test, .
As a beginner, at least you need to know what we do with Fisher's Exact Test (see ). My function is simple,
The definition of mutual information could resort to wiki:
For marginal mutual information, we say it is :
I(A,B)=sum sum P(A,B) log[P(A,B)/P(A)P(B)]
We use the idea of Chu-Liu/Edmonds Algorithm, see paper [1,2], to implement four functions here.
1. Maximal Directed Maximum Spanning Tree
2. Minimal Directed Maximum Spanning Tree
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