endeavor
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Given that the joint probability Pr(w,x,y,z) over four variables factorizes as
Pr(w,x,y,z) = Pr(w) Pr(z|y) Pr(y|x,w)Pr(x)
show that x is independent of w by showing that Pr(x,w) = Pr(x)Pr(w).
Attempt: if we simply assume Pr(x,w) = Pr(x)Pr(w), then:
<br /> \begin{align}<br /> Pr(w,x,y,z) &= Pr(w) Pr(z|y) Pr(y|x,w) Pr(x)\\<br /> &\stackrel{?}= Pr(x,w) Pr(z|y) Pr(y|x,w)\\<br /> &\stackrel{?}= Pr(z|y) Pr(w,x,y)<br /> \end{align}<br />
But can we say the last line equals Pr(w,x,y,z)? I think this problem can be solved by simply applying Bayes' rule several times, but I can't seem to wrap my head around it.
Pr(w,x,y,z) = Pr(w) Pr(z|y) Pr(y|x,w)Pr(x)
show that x is independent of w by showing that Pr(x,w) = Pr(x)Pr(w).
Attempt: if we simply assume Pr(x,w) = Pr(x)Pr(w), then:
<br /> \begin{align}<br /> Pr(w,x,y,z) &= Pr(w) Pr(z|y) Pr(y|x,w) Pr(x)\\<br /> &\stackrel{?}= Pr(x,w) Pr(z|y) Pr(y|x,w)\\<br /> &\stackrel{?}= Pr(z|y) Pr(w,x,y)<br /> \end{align}<br />
But can we say the last line equals Pr(w,x,y,z)? I think this problem can be solved by simply applying Bayes' rule several times, but I can't seem to wrap my head around it.