Maximum Likelihood Estimation and Bayesian Estimation

Introduces the maximum likelihood and Bayesian approaches to finding estimators of parameters.

[robertmatthews]

Wanted a quick intro, and this was fantastic; so clear and grounded in real-world issues. Guess that reflects the applied background of the from maths and physics depts is generally incomprehensible and bogged down in technicalities.

Thanks for creating - let's have more !

[LearnFinnishBySinging]

Possibly a minor correction: at 7:08 f(x|alpha) is the likelihood, and not the prior; f(alpha) is the prior (initial degree of belief in parameter alpha)
Thank you for the video!

[mehreenkanwal]

i find this video very helpful for my assignment, which was about difference between MLE and bayesian approach, thanks Alot to define it in minutes what i was trying to understand from hours on web

[renukadolkar]

Thanks! This video clear a lot of the basic concepts that I was trying hard to grasp in my Statistical Machine Learning class. Please keep up the good work Sir!

[ankitagarwal]

Thanks for all your efforts They help a lot and encourages us.

[sanjaykrish]

Very good summary of estimation techniques.. Very helpfull

[DarkFalco]

Always, in the asymptotic case, MLE can achives smallest possible variance of any unbiased estimator?

[zhengjia]

Hi, could you explain at 11:09 why Baysian allows us to find the best estimator? Because of prior knowledge incorporated?

[giovannilabate]

Dear Professor, what kind of presentation software do you use?

[Bobbel]

Thanks! Excellent introduction to the estimator classes.

[abbashoseini]

best if the bests :D thank you so much.