Web: https://www.reddit.com/r/MachineLearning/comments/se3j0q/d_looking_for_explanation_of_conditional_gaussian/

Jan. 27, 2022, 5:22 p.m. | /u/Solid-Initiative-153

Machine Learning reddit.com

Hello, I am reading Bishop's PRML book and trying to solve problem 2.16 (given in the picture)

https://preview.redd.it/esfj9z89l9e81.png?width=850&format=png&auto=webp&s=8f89ec8b9cd4322a80d518dfcd396072e9ee4c3e

I am having a hard time understanding the conditional probability p(x|x2 ) = N (x|μ1 + x2 , τ1^(-1) ), which is given in the solution. How did we get μ1 + x2 for the mean and τ1 for the precision?

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