"Proving Variable Independence in a Network Using Markov Blanket"

AI Thread Summary
The discussion centers on the concept of variable independence within a Bayesian network, particularly in relation to a variable's Markov blanket. A variable, referred to as node A, is considered independent of other variables in the network that do not belong to its Markov blanket. The participants seek a straightforward proof of this independence without delving into complex probability formulas. The term "independent" is defined in the standard statistical sense, emphasizing the desire for clarity and memorability in understanding these relationships. The conversation highlights a mutual interest in discovering and sharing insights on this topic.
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Is there a simple (meaning, memorable and not just a lot of crunching through probability formulas) proof that a variable is independent of the other variables in the network, given its Markov blanket?
 
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Were you able to get this yet?

Is "a variable", node A in the Markov blanket? And "the other variables in the network" any node that does not belong to the blanket?

If so, then define independant?
 
--No, I have not yet found this out

--A variable is a node in the Bayesian network, together with its conditional probability table

--"Independent" is used in the standard statistical sense
 
If you get it, let me know. I'd be interested to hear. I'm sure I'll realize it in due time, and get back to you if you don't to me first.
 
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