Start the discussion about BDe scoring module

[cwyoo]

Here are some dot file examples.

Also please post associated datasets as well.

[lsand039]

Here are the data files associated with the structures.

[qja0428]

This week, I implemented two function in the class graph, one is loading dot txt file into graph, the other is transforming graph to a dot txt file and save it in the specific directory. I have tested, it work fine, and I have uploaded all the code files onto our gitlab, thanks!

[qja0428]

Here are the data files associated with the structures.

Hi Lauren, I have checked your data, there are some problem about your data: first the data format should be csv file; second in the data set each column should be one variable, each row should be one observation; third in my code, I assume there is no row name in the data set and there should be a header (variable name) in the data set. So I think you should clean your data first, and then the code will work.

[cwyoo]

Here are the data files associated with the structures.

Hi Lauren, I have checked your data, there are some problem about your data: first the data format should be csv file; second in the data set each column should be one variable, each row should be one observation; third in my code, I assume there is no row name in the data set and there should be a header (variable name) in the data set. So I think you should clean your data first, and then the code will work.

I will suggest you commit your code in SMLG Git server under public domain and write how to run it at:

Board index ‹ Manuscripts & Documentation ‹ Implemented Classes/Modules/Programs Documentation ‹ How to run scripts/programs

[qxin001]

Concept learning for Bayesian Network:
1.Bayes Rule P(A|B)=P(B|A)P(A)/P(B)
2.Likelihood-How probable is the evidence given that our hypothesis is true?
Prior-How probable was our hypothesis before observing the evidence?
Posterior-How probable is our hypothesis given the oberserved evidence?
Marginal-How probable is the new evidence under all possible hypotheses?
P(H|e)=P(e|H)P(H)/P(e)

what we want to know : p(s|x) posterior
what we should know: p(s|x)=p(x|s)p(s) likelihood *prior
background knowledge: p(s|x)=p(x|s)p(s) p(x|s) is maximum likelihood. p(s) is assumption.

3.A Bayesian Network consists below:
A set of variables and a set of direct edges between variables
Each variables has a finite set of mutually exclusive states
The variable and direct edge form a DAG (directed acyclic graph)
To each variable A with parents B1, B2 ..Bn there is attached a conditional probability table P(A| B1, B2 .. Bn) ------------reference from [Jensen, 1996]
Jessen F., An Introduction of Bayesian Network, Springer-Verlag,1996

4.Three Bauesian Network Structure connection
diverging connection P(A,B,C)=P(B|C)P(A|C)P（C）
serial connection P(A,B,C)=P(B|C)P(C|A)P(A)
converging connection P(A,B,C)=P(C|A,B)P(B)P(A)

5.Bayesian belief network For instance, if we already know a CPT of LungCancer, we could calculate the probability -P("Smoker")*P("FamilyHistory")*P("Lungcancer"|("FamilyHistory,Smoker")). We will get more accurate result.If each conditional probability is unknown, we should use EM algorithm or others. If the data structure is unknown, then we should use K2 algorithm(greedy search) to solve this problem based on the data we already know.