2.3.1 Conditional Gaussian Distributions - Pattern Recognition and Machine Learning

We consider the situation of jointly Gaussian variables and show that the distribution when condition on one set is also Gaussian. We demonstrate how the mean and covariance of the conditioned distribution can be easily read off by completing the square. We derive expressions for the mean and covariance of the conditional distributions, both in terms of the covariance and in terms of the precision of the joint distribution, meeting the Schur complement along the way.

[@sabelojupiter6081]

Great series

[@gender121]

I tried so many times to read the book but could not understand properly.
Please continue to complete the book. Really great work and you will remembered for long time.

[@user-mt4li6jt9m]

i'm still in the first video of the first chapter
but I find this video just get uploaded
and I want to thank you and I hope u continue working on this book

[@josejavierandrescarrasco8652]

You are the best. Please continue , I really enjoy how you explain each one of the chapters of this book.

[@TheProblembaer2]

which book?