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? 
Re: Clarification of Conditional Probability Interpretation
Never mind: the joint probability distribution actually integrates to 1 so the formulation is correct. How can I delete the thread?

All times are GMT 7. The time now is 11:04 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2019, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.