Introduction to Bayesian statistics, part 1: The basic concepts 

Wow, my understanding acquired from this video is more than from dozen of hours on classes.

That was excellent explanation of the interaction between the parameters, thank a lot for putting the time and effort to do the animations

This is the best introduction to this that I've found online! Thanks!

What to say, an excellent explanation of Bayesian updating, long life to Stata and its People!

It was the most comprehensive video with the amazing explanations about prior, likelihood, and posterior. Thank you so much for this wonderful video.

excellent explanation. I had been surfing internet, for clarity

This is awesome. So intuitive and interesting. Why did we ever use null hypothesis testing? With the computational power we have now, this should be the norm.

Thank you Sir, the best explanation I found on youtube..

Your teaching style is very effective. Explanation and pacing is very good and your voice maintains attention very well. Thank you for making this video, it was quite informative.

Thank you very much for the explanations of non-informative prior and informative prior. Very helpful for my research.

Finally I understand this thing. Thank you.

Thank you for making this video. I took statistics class before, but my knowledge is limited. Please add descriptive details so I can understand your video.

Posterior is proportional to the MLE x prior , not equal =

great vid! so informative

Thanks . I love statistic.

Amazing! Thank you so so much! :)

Thank you for this video its clear to me

.75x speed

excellent explanation sir.....

Thank you for your kind help.

Awesome, thank you! Animations are really helpful.

Thank you. The first video that makes me understand this reasoning in one go.

Brilliant video thank you a lot

that was so so helpful. thank you.

How can we specify belief power of prior? In this example alfa, beta=30. And we can assign 250 for both. There is no boundary for us to prevent assigning 250 instead of 30. In a real life data, if you assign powerful prior, this means you have a bias and you may have implemented pressure to information coming from data; otherwise you have come close to non-prior case.

If the coin is held with heads facing up, what is the likelihood it will yield heads when it is tossed? If the con is held with heads facing up, what is the likelihood it will yield tails when it is tossed? If the coin is held with tails facing up, what is the likelihood it will yield tails when it is tossed? If the coin is held with tails facing up, what is the likelihood it will yield heads when it is tossed?

great explanation

Thank you. That was very clear and helpful.

excelent video

amazing! thanks!

How is it that you are able to neglect the probability of y for the posterior distribution function, which is normally on the denominator?

What i dont understand is how is multiplying liklihood and prior distribution going to give us what we call the posterior distribution. If anything the product just seems like a random function

I have the same version of Stata as yours. However, my Bayesmh window doesn't have the "univariate distribution" option. What could be the reason? Can you give me a hint?

One question I would have on this, is how can you be sure you are not biasing your result using these informative priors? I believe the most conservative approach is indeed the uniform (equivalent to I don't know anything so everything is equally possible for me), but when I start getting "clever", choosing appropriate priors, I can't make a real hypothesis test with that because I already tell the coin to be 50:50 (while someone could have potentially given me a magic coin of 10:90).

I shouldn't be saying that loud but dunno about you, I find this prior distribution & Ledoit-Wolf shrinkage method for accrued efficiency very difficult to picture and don't get me started on these affecting eigenvalues instead of eigenvectors... it's a mess in my head right now... I really need to pull myself together

At 1:40, shouldn't the area under the graph be equal to 1? What does the y-axis represent?

"Non technical" 3:07 Right.

@4:30 what's the difference between credible interval and confidence interval? After reading about it made me even more confused...

Maybe the video creator intended to explain Bayesian statistics, but did not. The concepts start to be explained, then there is a stepwise jump into mentioning prior and posterior probability, with the introduction of on screen equations but no further explanations - it's like it was read out of a technical manual that only 'insiders' know about. This then quickly turns into how to use the software/which buttons to press, which seems applicable to those who already know about Bayes and want to use the software - and not for those who want an introduction. So I'm sorry to say this video was not useful to introduce Bayesian statistics and I would recommend giving it a miss.

Hi, thanks for the video. What I wonder is, what are " default priors" when it comes to bayesian inference? As I understand, the priors are specific to each hypothesis or data, so how come some packages include these defaults? What do these priors entail?

Your animation were based on binomial likelihood and in Stata you choose Bernoulli likelihood are they the same if we remove the binome factor (choose (N,X)

excellent sir

Thanks. Perhaps you do another video to call it part 0 as the building blocks for this part 1. Introduction that is :)

Hi can someone explain why this form of probability is important ?

Isn't there an error at 5:18 Shouldn't the beta distribution's a and b be 86 and 84 NOT 106 and 114 ???? as the mean of 86 and 84 gives the mean on the screen (0.506) ...... Whereas the mean of the beta(106,114) is 0.481

how to calculate odd ratio in bayesian ordered logistic plz tell me

Chuck the new stata 17.1 has different command structure. Can you please redo the video for version 17.1.

Hi, On what depends the type of likelihood distribution? Thanks,

why is the posterior narrower at 5:15?

Ok so how has the Bayesian model been tested and demonstrated superior to other statistical methods. I'm always skeptical without hard evidence.

1:25 Why does this mean? Prior = Beta (1.0, 1.0)

the coin could land on its edge, neither heads or tails. Forgot about that potential event didn't you.

would someone please tell me what is he saying at 0:28 ? thank you

DURING HIGHSCHOOL DAYS, MY CLOSEST FRIENDS ARE THE NICE ONES.

Please, could you send us the video transcript?

this is Advance basic concept.

i understand nothing

Please could you indicate some friendly material about bayesian inference?

There's no information about what the Y in the graph is/refers to. This is unacceptable

Woo

BAYESIAN STATISTICS IS AN EXTENSION OF THE CLASSICAL APPROACH. VARIOUS DECISION RULES ARE ESTABLISHED. THEY ALSO USE SAMPLING DATA. I LEARNED ABOUT THIS WHEN I WAS STILL IN HIGHSCHOOL IN ATENEO DE ZAMBOANGA UNIVERSITY, MY GRADES IN ALGEBRA ARE HIGH.

Proving the non-existence of God was harder than I thought.

This is a very bad introduction. You jumped from the absolute basics to straight up prior and posterior. I'm really tired of these videos that area dvanced videos as "beginner videos" in disguise. They really spam all of RU-vid but don't provide any value. Please explain it more simply next time and please elaborate what each concept means that you introduce within a few seconds. Sorry for being this critical but I'm not here to learn and not to waste my time.

Really bad video for a newbie trying to learn Bayesian statistics

what tHE BLEEP did he just say?

too many basic errors: "distribution closer to .5" such a claim is not even formally defined

so fucking fast..