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Very helpful! Appreciated

thank you

I understand that in the coins example in the beginning, you used the binomial distribution to get the likelihood. Is it true that you have to replace that with the log likelihood ?

Very good presentation.

Amazing explained 🎉

Shouldn’t the variance of the first data point be (X1-Xbar)/1 or (X1-Xbar)/0 which is just 1 as we want the variance of just first data point whereas sigma^2 is the variance of the whole sample?

thanks it is so important explanation. keep it up!😍😍😍

I completely understand it! Thank you so much.

shukriya bro <3

thanks very well explained

Hello. thanks. we will be so glad to see more videos from you about Beysian estimators and MAP. Better explaining than my professor. thanks again.

This video is very easy to understand. Thank you so much

This is very helpful thank you

W video man, gotta love it when somebody knows what they are talking about!

You made it soo simple to understand. Thanks!!!!

wow such a great perspective , that is really a good video. i loved how you explained with different scenarios that how removing outliers would affect. the only small issue was your pace please try to speak a little slow rest everything was really good.

Love you!

Thank you so much one of the greatest videos!

I failed statistics course twice and I'm studying for my third final exam. This is the first time ever I completely understand this concept. Very very helpful video. Thanks a lot.

Excellent! Can you provide an example of an estimator that is both biased and inconsistent? My thoughts - if we consider a sample X1,...,Xn and set 𝜇^ = max{X1,...,Xn} as an estimator for the population mean then it will be biased and inconsistent; do clarify.

We learnt this concept in Probab. And Random Signal Princ. lecture and now I am trying to use it on the antennas, good explanation thanks

Thank you for this! Very helpful. Good content and a nicely structured.

I have watched three videos on this subject. I am still non the wiser, just better informed!

imagine the power of this man with manim

There is only one flip of a coin, just as there is only one election. It makes sense for an F to say "given two sides, it's 50% probability the NEXT flip will be a head. It makes sense for a B to say "given the mood of the country, I'd wager even money the NEXT election will see X win." Our two punters, F and B, are using different forms and sources of data and related factors, but both are willing to wager at odds. That's the test of sincerity, willing to bet on risk vs. reward. Answer Darwin's question: is that ripe fruit worth the risk of a predator?

crystal clear explaination

People in China like pasta too tho..

Sir, your videos are amazing!! Thank you Please keep on uploading more

This complex topic could only be explained this easily and with such simple examples.

This was the best explanation of the MLE on youtube.

Excellent Explanation

this video is so well made marry me

thanks for lecture

Hey brian, neat video as always! Could you made some videos on hypothesis testing, the neyman pearson's lemma, UMP test and Generalised likelihood ratio test? Thankyou in advance

most simple yet in depth analysis found so far, very intuitive..thanks a lot, because of you all my doubts got cleared

Thanks. Never thought it would be that easy.

You explained nicely, great job

If the one explaining truly understands the concept well, his explanation will be easy to understand. So, if I haven't been able to grasp my professor's convoluted explanations all this time, it could be because he doesn't actually understand the concept very well.

thank you so much! this helped a lot 😊

awesome explanation.

9:42 Is the log(0.8) meant to be log(1.8)? Nice video btw.

Thank you, but one thing I still don't get about about the Bayesian way of thinking: if one is supposed to constantly update a belief via data, then where did the prior come from? How does one quantify a belief based on zero data?

Great explanation Brian! I have a small question, though. If the response variable has to be normal (in a normal linear regression), why do you think most statistics articles insist that only the residuals have to be normal and not the variable? What tests do you think should be done before a GLM, besides residual plots?

Amazing. Enlightening

Love the video. These concepts are explained very nicely and in a easy to understand manner.

Very clear explanation. Thanks.

great content, keep it up

Is there any reason to have two different ways of solving it? One at 5:43 and another at the end. Which one do you think is better and why ?

you have great videos !! thank you so much <3