Introduction to Bayesian statistics, part 1: The basic concepts

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Information Introduction to Bayesian statistics, part 1: The basic concepts

Title	:	Introduction to Bayesian statistics, part 1: The basic concepts
Lasting	:	9.12
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听不太懂啊，这上面怎么都是没字幕的啊
Comment from : @xianjunzhang-d8u

man, FINALLY I get to understand (and visualize) these concepts I spent nearly a year trying, but it was always a complete fog Subscribing now, from Brazil!
Comment from : @wapsyed

And this is an explanation for who? For a bunch of statisticians? Certainly not for the 'unintroduced'! A few seconds into the 'introduction' and you are already using highly technical jargon! Perhaps you need a course in pedagogy first! 😅
Comment from : @ijexcmos8153

What informs the choice of a beta?
Comment from : @jokyere1553

What to say, an excellent explanation of Bayesian updating, long life to Stata and its People!
Comment from : @pep_4_climate

Amazing! Thank you so so much! :)
Comment from : @shaswatachowdhury9032

Really bad video for a newbie trying to learn Bayesian statistics
Comment from : @Pappa261

This is a very bad introduction You jumped from the absolute basics to straight up prior and posteriorbrI'm really tired of these videos that area dvanced videos as "beginner videos" in disguise They really spam all of Youtube but don't provide any valuebrbrPlease 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
Comment from : @nano7586

Thanks I love statistic
Comment from : @francescos7361

"Non technical"brbr3:07brbrRight
Comment from : @flake8382

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
Comment from : @MA-rc2eo

If the coin is held with heads facing up, what is the likelihood it will yield heads when it is tossed?brIf the con is held with heads facing up, what is the likelihood it will yield tails when it is tossed?brIf the coin is held with tails facing up, what is the likelihood it will yield tails when it is tossed?brIf the coin is held with tails facing up, what is the likelihood it will yield heads when it is tossed?
Comment from : @kathyern861

the coin could land on its edge, neither heads or tails Forgot about that potential event didn't you
Comment from : @kathyern861

Thank you Sir, the best explanation I found on youtube
Comment from : @SoumyadeepMisra7

excellent explanation I had been surfing internet, for clarity
Comment from : @divyateja939

Hi can someone explain why this form of probability is important ?
Comment from : @Drockyeaboi12

DURING HIGHSCHOOL DAYS, MY CLOSEST FRIENDS ARE THE NICE ONES
Comment from : @alexismarquez3674

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
Comment from : @alexismarquez3674

amazing! thanks!
Comment from : @josefwang

It was the most comprehensive video with the amazing explanations about prior, likelihood, and posterior Thank you so much for this wonderful video
Comment from : @AradAshrafi

@4:30 what's the difference between credible interval and confidence interval? After reading about it made me even more confused
Comment from : @haneulkim4902

Chuck the new stata 171 has different command structure Can you please redo the video for version 171
Comment from : @at6969

Thank you for this video its clear to me
Comment from : @learnandevolve4246

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
Comment from : @ngm_4092

Ok so how has the Bayesian model been tested and demonstrated superior to other statistical methods I'm always skeptical without hard evidence
Comment from : @jamesbowman7963

Brilliant video thank you a lot
Comment from : @ksspqf6016

excelent video
Comment from : @danielnakamura6430

Proving the non-existence of God was harder than I thought
Comment from : @ohnsonposhka9891

Your animation were based on binomial likelihood and in Stata you choose Bernoulli likelihoodbrare they the same if we remove the binome factor (choose (N,X)
Comment from : @WahranRai

that was so so helpful thank you
Comment from : @matthewcover8748

this is Advance basic concept
Comment from : @epicwhat001

Thanks Perhaps you do another video to call it part 0 as the building blocks for this part 1 Introduction that is :)
Comment from : @Rainstorm121

Woo
Comment from : @뭐냐-j7y

i understand nothing
Comment from : @yuuki7831

excellent explanation sir
Comment from : @sujiththiyagarajan4290

so fucking fast
Comment from : @jinudaniel6487

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

what tHE BLEEP did he just say?
Comment from : @mikesilva6521

would someone please tell me what is he saying at 0:28 ? thank you
Comment from : @nadineca3325

There's no information about what the Y in the graph is/refers to This is unacceptable
Comment from : @jack8831

Thank you very much for the explanations of non-informative prior and informative prior Very helpful for my research
Comment from : @emilyzheng1

Thank you for your kind help
Comment from : @Pankaj-Verma-

great vid! so informative
Comment from : @yanchen3129

why is the posterior narrower at 5:15?
Comment from : @XBrynnerX

Would like to share the details of the following booksbrBayesian Methodology: An overview with the help of R software br wwwamazoncom/dp/B07QCHTR54 - E-book br wwwamazoncom/dp/109293989X - Paper back brISBN-13: 978-1092939898 brInternational Journal of Statistics and Medical Informatics brwwwijsmicom/bookphp
Comment from : @editorijsmi7830

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?
Comment from : @Jdonovanford

Wow, my understanding acquired from this video is more than from dozen of hours on classes
Comment from : @vietta9204

Please, could you send us the video transcript?
Comment from : @andreneves6064

great explanation
Comment from : @valor36az

Please could you indicate some friendly material about bayesian inference?
Comment from : @andreneves6064

how to calculate odd ratio in bayesian ordered logistic plz tell me
Comment from : @rizwanniaz9265

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)
Comment from : @erggish

Finally I understand this thing Thank you
Comment from : @bigfishartwire4696

75x speed
Comment from : @ohmyfly3501

This is the best introduction to this that I've found online! Thanks!
Comment from : @SuperDayv

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
Comment from : @jehangonsal2162

That was excellent explanation of the interaction between the parameters, thank a lot for putting the time and effort to do the animations
Comment from : @ahmedmoneim9964