Thank you sooo much for this

i am so grateful for your videos, this is the first time i have fully comprehended this subject. thank you for sharing!

Bayesian stats made me multiply multivariate probability density functions by other PDFs, and I'm still mad about it.

thank you so much for explaining with such clarity

I’m so confused 😢

Thankyou so much for this amazing video lectures 🙏 love and respect from India keep uploading more videos on mathematical statistics

Thanks ma'am for this explanation. It was clear and concise.

I would not have passed advanced statistics without you - thank you so much for these videos!

nice!

is it not (1+theta)^-1

why is it mu - ybar in the likelihood and not ybar - mu?

this is simple, straightforward and easy to understand, walked me straight through the steps of deriving exponential family for a Normal distribution, thank you so much!

Super helpful!

thankyou very much mchinkuuu

could you explain what's the logic to go from "this is proportional to" to "this is equal to"?

Thank you so much for these videos! I feel like I can actually understand this now haha

thank you. im still wondering where you got phi at 4:14. it felt arbitrary.

Thank you for this video!! Super helpful and well explained!

❤❤

Thnk you ❤❤

very goood👏

Thanks a lot!

thank you, great explanation

perfect.

Thank you so much madam professor.

Great video 😊 Please when the expression nlog(1-p) was expressed in terms of theta it turned negative that is -nlog(1+e^theta) and the b theta function is being subtracted from x theta, so by substitution the negatives should turn positive... Kindly clarify that portion for me. Thank you.

we thank you so much ma'am🙏🙏🙏well explained, we appreciate you

So mu ~ N(m, s2) and yi ~ N(mu, sigma2) ?

please calculate bayes factor for this

Thanks

Wow thanks

Thanks a lot. 🙂

Thank you for saving my head.... I was constantly trying to understand the steps.... My text book has directly jumped to the mean and variance without any steps in middle ....

so much easier to understand now! My lecturer had confused me

5. Today, Sasha checked their weight several times with different scales observing (in kilograms): 92, 82, 83, 86, 86, 90, 83, 84, 89, 85. Assume that the data is normal with variance σ 2 = 9 and a prior distribution for the true weight µ ∼ N(80, 100). (a) What is the posterior distribution? (b) Compute the credible interval of 95% for µ a priori and a posteriori. (c) Compare both intervals with the frequentist 95% confidence interval. Can you conclude that I was optimistic?

Hey, it should be n*y_bar^2/sigma^2 in the constant term. I know this is not important haha but just a reminder lol

I really like your video . thanks..good bless for you. do you have instagram maybe?

Thank you

This result proved false: Counter example: n=2, y_1=0, y_2=0, \mu=0, \sigma^2=1. Then f(y_1, y_2|\mu, \sigma^2)=N(0|0, 1)*N(0|0, 1)=0.3989*0.3989=0.1592 which is different from N(\mu|\bar{y}, \sigma^2/2)=N(0|0,0.5)=0.5642. Can anybody guess what is wrong in the proof?

Thank you for the very intuitive explanation.

DO WE NEED TO INTRODUCE Y VARIABLE AT ALL WE CAN CONCLUDE IT FROM THE X

why didn't my lecturer mentioned this but he just conclude what is min suff so confusing at the time

You‘re videos are a blessing. So clear and reduced to the relevant information. Thank you very much!

You are amazing at teaching! Thank you so much!!

The Indian teaching method is easier than this

Mrs, Can you derivate Maximum Likelihood Estimation for Inverse gaussian 3p please

if Mu_l = y.bar, then mu_l ^2 does not equal (1/n) sum(y_i^2) and it doesnt match up with what you had before

can you plz tell me how this e^theta/1+e^theta comes? plz help with this OR give it any reference

Really helpful by doing all the steps so clearly!! All series about the exponential family is so awesome, a lot of help for my exams

oh! amazing please finish it. thank you vary much!