#1




Clarification of Conditional Probability Interpretation
Let's say x is determined by a normal distribution and p(yx) is a sigmoid function if y = 1 and the sigmoid reflected along the vertical axis if y = 1.
p(yx) = { f(x) if y = 1 1  f(x) if y = 1 } Then p(x, y) = p(yx) * p(x) So for the case y = 1, does it make sense to say that p(x, y) is just the multiple of the sigmoid function and the normal distribution? 
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