How Bayes Theorem works
Vložit
- čas přidán 31. 10. 2016
- Part of the End-to-End Machine Learning School Course 191, Selected Models and Methods at e2eml.school/191
A walk through a couple of Bayesian inference examples.
The blog: brohrer.github.io/how_bayesian...
The slides: docs.google.com/presentation/...
Follow me for announcements: / _brohrer_ - Věda a technologie
![[柴犬ASMR]曼玉Manyu&小白Bai 毛发护理Spa asmr](http://i.ytimg.com/vi/0TsXQ7z2Dh4/mqdefault.jpg)
Wow best explanation and example ever I saw ^^ Fantastic.
Excellent
exactly. These pacient disease examples were driving me nuts.
this is by far the most accessible explanation of Bayes theorem. Well done Brandon!
Best explanation of Bayes theorem I have seen. Fantastic teaching.
This is the first time I've felt like I've actually understood this... and it's such a simple concept! Thank you!
EXCELLENT EXPLANATION!!! I am learning graphical modeling and a lot of these concepts were a bit unclear to me. Examples given here are absolutely to the point and demystify a lot of concepts. Thank you and looking forward to more videos.
This is the most accessible explanation about Bayesian Inference. Thank you Brandon for the time taken to prepare this video. You rock !
Thank you for this excellent explanation. You are a patient and well-spoken teacher.
The way you connect things with appropriate easy to examples...Amazing...
This is fantastic. Thank you so much! I have been exposed to BT before, but have never understood it. As sad as it sounds, I didn't realize it was composed of joint probability which is composed of conditional probability, and marginal probability. Conditional probability and joint probability, and Bayes theorem all just looked the same. This really helped clarify things for me.
I have viewed many explanations about Bayes rule but this is no doubt the best! Thanks Brandon
Loved the analogies with real life philosophies, brilliant!
Great example! Very easy to follow and understand. On a side note: I showed your video to my students and some of them objected rather "emphatically".
They said it was too sexist. Crazy times we live in....Instead of math and statistics, they wanted to discuss gender roles and stereotypes in a Stat class. Gosh!
Thanks John! I agree with your students. When I watch this now, I cringe. I definitely need to re-do it with a better example, one that doesn't reinforce outdated gender norms.
@@BrandonRohrer No....Please, do not follow the mad crowds... this an innocent, simple math example. People are getting crazy and finding excuses to feel offended and start meaningless fights!
So sad. Long hair and standing in the womens restroom line and we cant even use Bayes' thereom to assume its a woman 😂
Thank you sooo much Brandon for explaining the concepts so clearly.
Definitely the best explanation of the theorem told in an easily understandable way, I can find in the internet...
I have been struggling with bayesian inference and your tutorial makes it so easy to understand! Thank You! Keep up the good work.
I'm very happy to hear it Fahad. Thanks.
when your teacher don't make sense, had to go through teaching videos online and came across this one...
Lucky lucky lucky! Thank you Mr!
Amazing. I already knew what Bayes theorem was, but you have an awesome intro to Bayes. Thanks for the video.
Great explanation and video lesson production. Best Bayesian lesson I've found on youtube
Stop looking for a descent tutorial... this one is the best!
This video deserves more thumbs up. I understood a lot on a lazy sunday evening :) great explaination.
I must say, this is the explanation of Bayes theorem, I have ever seen..... PERFECT!!!!!
This was the best explanation of Bayes I've ever heard, I had such a hard time wrapping my head around it from other sources
Best video I found with all the information that I needed at one place.
Thanks.
Brandon I just want to tell you that you are a fantastic teacher.
Thank you very much Shirshanya. That is a huge compliment. I'm honored.
Please make more statistics videos! I have suggested your channel to my biostat teacher.
I was looking for intuitive content to introduce me about essence of Bayes theorem in statistics, thanks for this. Luckily I found your blog about machine learning and robotics. That's all what I wanted under a roof, robotics, data science and machine learning.
i don't know what to say, i'm a computer science student and i have never seen an explanation better than this ... thank you veryyyyy much
I wish everyone taught like this. Your presentation was awesome. Thank you
Excellent explanation ! This is the manner in which mathematics must be explained. With cases of practical applicability. Good job mr. Brandon !
Thanks Razvan. I'm so happy you enjoyed it.
Excellent explanation. At the 15:20 and beyond is when everything really started to come together. Also thanks for deriving the formula at the 7:10 mark.
I have been searching for explanation like this for sometime and a big WOW to this guy. Wonderful explanation!!
This is the best explanation yet, it helped me get a greater intuitive sense of Bayesian inferences.
Yes it was great. It seems running into Feigenbaum maths or simular
hands down best explanation i've seen, thank you
You're very good at explaining and also you go in some details which is nice. Too often youtube tutorials are too simple. keep going.
now that you have said that (an year ago), i kinda feel like finding the probability of likelihood of a youtuber making too simple tutorials!
Great video!! Very nice and easy to digest explanation of Bayes theorem! Thank you very much for sharing this excellent material. I have got a better understanding on how to apply it to my problems. Keep the great work!
Excellent examples and explanation! Now everything is so much clearer. :)
I know the video is old but I have to agree with the pinned comment. I already knew Bayes Theorem buy as I don't use it often, I have to be constantly refreshing the details in my mind. CZcams algorithm recommended this video and it's hands down the best I have ever watched.
Thank you. I really appreciate that.
For 5 years i kept Bayes aside , you are the guru in teaching stuff.. God bless you Brandon
Thank you. Your video has been of great help. I have tried different resources to wrap my head around Bayesian theorem and always got knocked out at the front door. Excellent expalnation
Thank you for the video, it helped me understand the concept of Bayesian inference.
The concept is simple. In a nutshell, you have an idea about what the quantity is and then you use the measurements to sharpen your assumption.
best explanation I found on the topic so far. great work!!!
Terrific examples and terrific explanation down to such applicable quotes!
Thanks for the excellent video. A good refresher! Keep up the good work!
I was reading about Bayes Theory for months ! And this is the first time I understand the concept!! Wow!! such an amazing way of teaching!!
I'm so happy to hear it Taghreed. That was exactly my hope.
Superb lecture - esp. the MLE explanation!
the best explanation I ever seen! Super clear.
Great explanation and simplification of a difficult concept. The three quotations at the end are poetic and purposeful. Thanks
I found them surprising relevant too. Thanks Sridhar.
Hi Brandon, your video was simple, superb, and stupendous!
Simply the best ! Thank you Brandon
hey man,this is the one good explanation for conditional prob i had ever heard
Thanks!
Amazing videos! Thank you for making my day easier.
Absolutely brilliant! Your presentation, examples, etc. were perfect and applicable! Thanks!
Thank you very much :)
Brandon, GREAT explanations!! I am taking a "Math for Data Sciences" class and have been flying through it until the final week and "Bayes Theorem". Achk...... It was poorly explained and very confusing. I was going to drop the class as I just couldn't get it. After watching your CZcams explanation I am excited about the possibilities and understand the way it works - cool stuff! Thank you for all you do!!!
this was very intuitive explanation, man do more!
Thank you so much. The way you put it is really nice and easily understandable, yet conveys the concept. :)
such a well-thought -through video, very good explanations for every instance, the ending was the bonus, loved it, thank you
Thank you Lilit! I appreciate that.
Bravo! Wonderful presentation. Thank you for this presentation.
the best explanation i hv seen about bayes theorem... awsome... thnx a ton....
i like the quotes you put at the end and how you reword them
There are never lines at the men's room.
haha.
lol
Unless it's cocaine.
So the probability of this sample being true is 0%, hahhaha
You have never been to developer conferences :)
Brandon, this was great, thank you. Very easy to follow and really interesting and concise!
Thanks Erin!
Thanks, very well explained, the charts are also very well prepared.
GOD THANKS FOR EXISTING. Finally somebody that fucking breaks down the most important part of it (namely, how the do you calculate the likelyhood in practice). I hope life rewards you beautifully.
Thank you for this excellent presentation!
Really I want to thank you so much
the best thing that I have ever heard about Bayesian.
you have illustrated everything and simplify the method for me
regards
The knowledge that Bayes was a theologian and that his theory requires at least some belief or faith in improbable things earns a "Well played, Mr. Bayes" slow clap. I've been enjoying your videos Brandon, thanks for keeping things approachable!
I’m not sure that Bayesian epistemology requires a belief in improbable things. I love this video, but I think that’s an overstatement. I do think that it requires us to be open to the possibility that improbable things may be true. It does not require me to have faith in anything improbable, but rather to proportion our beliefs to the evidence (probabilities)- which is the antithesis of faith-while accepting the possibility of being wrong. To accept something as true that is improbable… is intellectually irresponsible and lacks due caution and humility. But to withhold belief (proportionally to the evidence) from improbable things is intellectually responsible and does not exclude being open to surprise-to the possibility that something improbable is true. I don’t think Bayesian epistemology intrinsically expects you to hold as true some improbable thing (faith). Abstinence from faith is acceptable in all cases as long as the possibility of error is operative. This suggestion that it’s necessary in Bayesian epistemology to believe something that is improbable was the only sloppy part of the video, no? I’m open to correction…
I think you guys got it the opposite way, the video was trying to say, be open to believe in the improbable things that come from the data (evidence), rather than only holding on a prior belief.
I thought this was not only a great example of Bayes but also a nice intro for Cox's Theorem. Nice jobQ
* quickly looks up Cox's Theorem *
Why, yes it does Donna. Thank you! :)
hope you can do more videos!!! you are the best teacher!
best explanation on youtube so far
You're so much better than my Statistics teacher, thank you so much for this explanation!
Thanks Mathias!
Wow... amazing. You’re a great teacher!
Lovely Explanation! Thanks a lot Brandon Rohrer
Nothing much to say only thank you! you may have helped me in clearing my exam!
This helped me immensely, thank you so much!!
I believe you don't know much about statistics (the impossible thing), but I do believe you really know how to explain Bayesian Inference. Great video.
I loved the Mark Twain citation in the end! Great video thanks!
However I have one question, do you assume normal distributions of the likelihood? Why so?
absolutely amazing explanation
0:40 Bayes wrote 2 books one about theology and one about probabilty.
He planed to write a third book infering the existence / non existence of God with probability (likelihood distribution = humanity, Prior distribution= miracles !
The best explaination on youtube
thank you man
What a stunning explanation. Speechless
💜 🧡 🖤 💚 🤎 💛 💙
Amazing explanation and graphics!
Thanks!
I am from Brazil. What a fantastic explanation!
Thanks Jose! Welcome to the channel
Thanks for excellent presentation! One question though:
at 17:49 the P(m=[13.9, 14.1, 17.5]|w=17) is factorized as following:
P(w=17|m=[13.9, 14.1, 17.5])
= P(m=[13.9, 14.1, 17.5]|w=17)
= P(m=13.9|w=17) * P(m=14.1|w=17) * P(m=17.5|w=17)
then at 20:47 the P(m=[13.9, 14.1, 17.5]|w=17) * P(w=17) is expanded into:
P(w=17|m=[13.9, 14.1, 17.5])
= P(m=[13.9, 14.1, 17.5]|w=17) * P(w=17)
= P(m=13.9|w=17) * P(w=17) *
P(m=14.1|w=17) * P(w=17) *
P(m=17.5|w=17) * P(w=17)
how do you get that P(m=[13.9, 14.1, 17.5]|w=17) * P(w=17) is equal to P(m=13.9|w=17) * P(m=14.1|w=17) * P(m=17.5|w=17) * P(w=17)^3 ? Thx in advance!
i hate stats because of those things... my teacher was teaching us utility analysis, and says "satisfaction is measured in utils" to that i ask "tell me how satisfied are you with your job and answer in the form: n-utils...".
i still haven't got an answer!
Thank you very much for the best explanation, It's very interesting
Start with more slides like your last two. Thanks this was insightful.
Great video - this really helped a lot. Thank you so much.
I found this quite helpful in giving Bayesian probability a more intuitive appeal. Bayes idea had previously been presented to me as "a priori probability", and I had always been troubled by the a priori part. But I guess a good way to think about it is like this: When we say, "All else, being equal, such-and-such is the case," we mean (or ought to mean) "Assuming that the variables which we have not measured or aren't even aware of have the values they most likely have when our one measured variable has the value measured or assumed by us, then such-and-such is most likely the case."
Wow can't believe I only came across this video now. This is by far the best explanation on Bayes with great examples! Thanks @BrandonRohrer !! Love the example with the weight of puppy! May I ask if you have codes to deal with multiple priors/ multiple events? Say such as an extension of the weight of the puppy, if the weight change is more than one pound, plus she may be showing some other symptoms (say losing appetite), the likelihood of her being sick from something is x. Or even, losing appetite can be just due to weather being too hot. So the lost of weight of one pound from the last vet visit and losing appetite may not be significant at all and doesn't warrant multiple expensive test suggested by the vet.
The best video about Bayes! Thumb up
great video presentation Brandon. please try to apply more videos on other machine learning algorithms
That was fantastically done.
Thank you :)
Dude your analogies are on point.
Thanks Julian
Just wanna say this is amazing. Thanks for sharing.
Superb explanation Sir
You deserve many more subscribers dude
Really fantastic teaching! Thanks!
Thank you so much for excellent explanation
Wow that was an amazing explanation! Thank you 😊
Thanks Brandon! This was very clear and useful. :)
Excellent presentation👍
Thank you very much Brandon for this great lecture !! I was crawling the Web looking for a clear explanation of the topic and your lecture was the best by far. I have just a question, please how did you simplify the formula at 16.47? Is it because we don't assume any prior that we can remove it ? What about the marginal probability you also removed ?
I wanna actually start working on deep learning, precisely deep Bayesian networks, variational inference, autoencoders.. Please is there any materiel you suggest ?
Thanks
did you start working with any of those things eventually?