30: Maximum likelihood estimation

Рет қаралды 123,179

Matthew E. Clapham

Күн бұрын

Пікірлер: 39

@CharlotteC1990 8 жыл бұрын

Best MLE video on KZbin! Thank you :)

@hojkoff 4 жыл бұрын

Great explanation. Clear, structured and explained in simple understandable terms. Thanks for taking the time to put this together.

@argentina2152 7 жыл бұрын

Thank you so much. Now it all makes sense. I had difficulty grasping the idea of MLE, but with your explanation I feel confident going back to the lectures and being able to follow them.

@FloppyDobbys 7 жыл бұрын

Most clear explanation i have seen on KZbin thus far

@CC-op3ez 2 күн бұрын

Thank you so much for your video, especially for warnings about model selection and AIC. Would you please explain more (or give me some documents or references) about "do not combine model selection with hypothesis testing. The p value significance will be inflated because you are implicitly testing multiple hypotheses with model selection"

@shane1146 8 жыл бұрын

Matthew you are awesome. I wish you did a video on Bayesian too. Bayesian, MCMC one please??

@stevegyro1 6 жыл бұрын

Matthew you are outstanding as a teacher. Thank you for the many insights and teaching. -Steve G.

@alonsobentley3351 3 жыл бұрын

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@Maha_s1999 6 жыл бұрын

Fantastic! Thank you so much for this super clear exposition.

@franciscomendoza3778 2 жыл бұрын

Really nice presentation

@مصطفىعبدالجبارجداح 3 жыл бұрын

بارك الله فيكم وجزاكم الله خير الجزاء

@sukursukur3617 2 жыл бұрын

Yeaahh thats the clear explanation

@saurabh75prakash 6 жыл бұрын

Nicely explained, thanks!

@elliott8175 2 жыл бұрын

Thank you so much!! Such clear explanations!!

@RPDBY 6 жыл бұрын

AIC - the lower the better, LL - the higher the better, but both measure the same concept, so using both is a redundancy, one will suffice (as one will always go down when the other goes up judging by the formula). Did i get it right?

@profmo 8 жыл бұрын

Thank you for taking the time to make this video.

@joseluisredondogarcia5244 8 жыл бұрын

The last slide is gold

@joelmhaske8185 4 жыл бұрын

Seriously good!

@PedroRibeiro-zs5go 7 жыл бұрын

Great video man! Helped me a lot, all the best :D

@tag_of_frank 6 жыл бұрын

isnt the (probability of x given theta) = (probability of theta given x)(probability x)/(probability theta) If this is the case, the "likelihood function" as you defined, is it really equal to the probability of x given theta ? If so, why, since it is missing those two terms extra terms?

@cube2fox 6 жыл бұрын

Fahraynk I have the same question, please tell me when you found an answer.

@MM-du7je 6 жыл бұрын

That is true in a Bayesian setting, where the parameters in a given model are treated as being random. The point of maximum likelihood estimation, on the other hand, and Frequentist inference is we treat parameters as being static, such that we can estimate them. I wish the author had written in the more common notation for density for MLE, with f(x ; theta) instead of f(x given theta), it's confusing when this isn't known. Btw you are correct, so Bayes theorem works for densities too! p(theta) is the density of the parameter, p(theta given x) is the parameter conditioned on the data (the thing we want!) and p(x) the normalizing constant. Bayesian inference is basically the science of picking a prior based on objective/subjective mathematical means.