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?
