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Joint Probability Distributions: Marginal and Conditional Densities

Steve Brunton

34,556 views

Here we introduce two distributions derived from the joint PDF: the marginal and conditional densities. These are especially useful in Bayesian statistics, and also help us bring ideas from Calculus into probability.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

%%% CHAPTERS %%%
00:00 Intro
01:40 Defining Marginal Density
05:28 Exercise: Calculate Marginals
06:30 Conditionals in Joint Probability
09:01 Outro

8 Comments

@Youcancallmerom 1 year ago

i'm from Iran and right now we are living through very difficult times due to the war.Thank you for making learning feel meaningful.

@tododiaissobicho 1 year ago

Thank you so much for this playlist on probability! It's very concise, although still well explained and with good examples and graphics. Well done! You deserve many more subs!

@karlsorensen3214 10 months ago

Thanks for these videos.

@drskelebone 1 year ago

"I have too many parentheses here, but that doesn't really matter." Welcome to every live derivation I've ever tried to do. XD

@comelighttheway 1 year ago

:finger-red-number-one::finger-red-number-one::finger-red-number-one::finger-red-number-one::yt::yt:

@michaelchen2718 2 months ago

I DONT UNDERSTAND. why are we integrating out over ALL the values of y.

@Siigrit 7 months ago

yeah i have no idea lol

@riccvven2078 1 year ago

being honest the CDF is not that easy for a Gaussian, a checked and there is like a Errorf function, which makes it less intuitive than waht may seem, here i tried to replicated last 2 videos keeping x, or y constant, but overall doensn't sound an easy homework to test ahahah