Grateful to your animation team kudos to you and your team . You are basically changing the way school and education system worked for more than 150 years.😊😊
Can’t believe I bought three books to figure out what was show to me in two UA-cam videos. Fantastic job good sir. Love the content and just became a subscriber.
One of the best key points to take is the longer form of representation of conditional probability. Never seen this explained in such great and easy detail than here. The longer notation makes everything so easy and simple to understand. You've a great talent to make the subject easily comprehensible for everyone. No other video in UA-cam has put this concept in such a simple way to understand. Thanks & Triple Bam....❤
Just yesterday I was looking to refresh my knowledge on Bayes theorem when I saw this video appear on my feed. Thank you StatQuest! Best way to learn and review statistics!
I really like your videos so far, you are explaining everything enough in detail without making the videos longer than necessary. The clean animations and speaking are also godsent!
After this video, I wouldn’t say I understand because this is the first time I dove in this topic, but now I certainly have the idea and I’m fascinated! Great video
The "slightly" redundant notation is very important and I do agree that it deserves to be there/used. And, the/your explanation is excellent. Thank you.
I went headfirst into a grad school cryptography class with zero knowledge of anything math related past high school, and this has helped me out a lot. Thank you!
I started this video and instantly jumped to subscribe. I love stats and you are the BEST at teach stats! Had to come back and comment just to make sure you know it :)
if i ever become a professor (this is my dream), i will definitely recommend your videos for my students. your videos helped me sooo much on soooo many courses. currently doing my bachelors, wish me luck! thank you statquest!
Don't know how many ppl relate to it.. but unnecessary optimisations to notations has really screwed up the learning/understanding of concepts. A concrete example is @03:08 This was so spot on!!!
In most of the cases, what they teach , formulation and nothing else. This video teaches formulation as well as intuition. Great explanation Stat quest. I must say Tripple BAM
Wow. Thank you so much! I'm not doing anything with Bayesian probability.... but this was so good I had to watch it through! Subbed. I knew how to get a degree of belief and check it... but I didn't know WHY until your video and the "why" is so important.
Dear Josh, your videos are super fun to watch and learn as well as your book, hope you have a chance to revisit the idea of making a series of Bayesian Statistics in 2024. Triple KUDOs to you.
@@statquest I should be the one thanking you, you are absolute legend, literally I am not lying, I have never seen a lecture as clear as this and thank you for this service.
Josh!! Thanks a lot, I love your videos :) Please show us the hypothesis testing in Bayesian perspective. Looking forward to more videos with Bayesian statistics.
That's the plan. Unfortunately I have to write a book on machine learning first. However, as soon as finish the book I'll do a whole series on Bayesian statistics.
I'm not a fan of the "bam" technique; but, in my opinion the explanation is actually clearer, and more easily connected to real-world examples, than other videos which seek to explain Bayes' Theorem. Thank you! (Liked and subscribed. Will be looking at other videos of StatQuest.).
Hey Josh. This is a great video. You and Brandon Foltz should collaborate. Suggestion: A video applying Bayes Theorem to a continuous dataset would be very interesting.
This is the best explanation on Bayes' Theorem ever! However, It is so difficult for me to use the kind of basic examples that ilustrates the theorem, for the particular task of analyzing differential gene expression between samples. There are available several "bayesian methods" to analyze gene expression changes and I can not understand the relationship of these methods with the theorem's formulation based on conditional probability. I am looking forward to see the upcoming videos on the subject. Would you please dedicate some of them to such practical applications of the Bayes' theorem? Thanks a lot!
Yes, that's the whole idea of Bayesian Statistics and that is what I want to cover in the follow up videos. Unfortunately I have to write a book about Machine Learning first. However, as soon as that is done I'll make the videos about Bayesian Statistics.
@@statquest Good news, i passed :D With a 6/10. There were a few questions in there about random forest and PCA etc, and i was able to clearly explain it thnx to your videos. I now have machine learning and your videos are agian a great help. You channel trully is a gold mine for people who need to learn statistics ^^
Excellent video as usual. May I make a wish: would it be possible that you made a video about bayesian vs. frequentist statistics? Every now and then I stumble about this antagonism, but explanations are often kept on a very abstract level. It would be great if you could give a case study which exposed the different (and incompatible?) conclusions these methodologies come to.
The "left hand" sides of both conditional probabilities refer to the same yellow area, but the right hand sides refer to different areas. "doesn't love candy" refers to the 8 people that don't love candy and "loves soda" refers to the 7 people that love soda.
Thank you so much for another great video! I couldn't find another video from your channel in which you explain the theory's application. I believe I know it, but when it comes to application, I become very unsure.
A box contains 2 fair coins and 1 biased coin. The biased coin has probability of a head as 2/3. A coin is drawn at random from the box and tossed. Then a second coin is drawn at random from the box (without replacing the first one). Given that the first coin has shown head, what is the conditional probability that the second coin is fair? How would you apply Bayes' rule to solve about problem?
Thank you for your excellent videos. I just have a question about Bayesian Information Criteria. Using the equation BIC = -2*logL + log(numObservations)*numParameters, can BIC be negative? If yes, when comparing different models, should I choose the minimum value? For example, between -9 and -0.5 and +2, and +9 should I choose -9? Regards, Nasrin
Thank you for this and all of your many spectacular videos. I was wondering if you could do a StatQuickie or other such video on z-scores if you don't already have one available?
Excellent. Thank for clearing up the P(A!B) should be P(A&B!B). This has confused me for years. I would drop in "given" and replace it with "in". P(A!B) becomes P of getting an A in the domain B.
Thanks for the brilliant Video! It really helps me a lot. :) Besides, I have a question. As far as I understand, this Video explains about that Posterior Probability is closed to Likelihood X Prior Probability. Well I am reading a Paper about VQ-VAE and the author has written on the paper "the prior is learned rather than static", which I assume that is about prior distribution. I want to know if there's any difference between prior distribution and prior probability. Or are they the same? Or am I missing something important?
@@statquest so what's this guy talking about ? ua-cam.com/video/o6-HCOG1Cp4/v-deo.html The term "Sensitivity Analysis" has been thrown around by our statisticians a lot ! need to know what they are up to !!
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
I just ordered mine. It should be here tomorrow 🥳🎉
@@25BDominique2021 BAM! :)
Just did! Because you are a genius and I finally understand Bayes Theorum!
@@veeek8 HOORAY! Thank you very much! :)
Grateful to your animation team kudos to you and your team . You are basically changing the way school and education system worked for more than 150 years.😊😊
Can’t believe I bought three books to figure out what was show to me in two UA-cam videos. Fantastic job good sir. Love the content and just became a subscriber.
bam!
I minor in math in college 20 hrs ago and forgot almost everything. This channel makes everything so simple. Thanks Josh!
@@txreal2 Thank you!
Josh...you have no idea how life changing ur channel is in my journey of exploring data science
Thanks!
So true.. BAAAAAMMM, triple BAAMMM quadruple Bammm
one stat quest video a day keeps the failures away! Thanks Josh for the awesome content as always :)
Thank you!
One of the best key points to take is the longer form of representation of conditional probability. Never seen this explained in such great and easy detail than here. The longer notation makes everything so easy and simple to understand. You've a great talent to make the subject easily comprehensible for everyone. No other video in UA-cam has put this concept in such a simple way to understand. Thanks & Triple Bam....❤
Thank you very much! :)
Just yesterday I was looking to refresh my knowledge on Bayes theorem when I saw this video appear on my feed. Thank you StatQuest! Best way to learn and review statistics!
Thank you very much! :)
This is a much more intuitive explanation of Bayes' Theorem than the one I first learned with terms like hypothesis, evidence, prior and posterior.
Thank you! :)
My prior knowledge was confusing after watching this video my Posetrior knowledge is BAM
I really like your videos so far, you are explaining everything enough in detail without making the videos longer than necessary. The clean animations and speaking are also godsent!
Thank you!
Thank you! I didn't understand Bayes while I was studying in high school. Now I understand it! Your explain is so clear!
Glad it helped!
I´m a statistics student from Colombia and I want to tell you Your channel is awesome man. keep it this great job. Thank you. again Googleplex BAM!
Glad you like StatQuest!!!
That intro is worth the price of admission alone. Thank you for making this; just subscribed!
Awesome! Thank you!
I am an Indian and I love soda, but I love StatQuest more
Triple bam!!! :)
@@statquest Noice 👍 Doice 👍 Ice 👍 you BAMed Throice 👍
But I love candy bhai 😼
I believe your videos on UA-cam could make great substitutes for many of stats course content in universities around the world!
Thank you!
Agreed!
After this video, I wouldn’t say I understand because this is the first time I dove in this topic, but now I certainly have the idea and I’m fascinated! Great video
Thanks!
You are amazing! I was not understanding Bayes theorem no matter how I look at and now, after watching the video, I completely understand
Glad it helped!
You're notes about notation really helped me. I think that's where I got confused before seeing this.
bam!
The "slightly" redundant notation is very important and I do agree that it deserves to be there/used. And, the/your explanation is excellent. Thank you.
Thank you!
I went headfirst into a grad school cryptography class with zero knowledge of anything math related past high school, and this has helped me out a lot. Thank you!
Good luck! :)
I started this video and instantly jumped to subscribe. I love stats and you are the BEST at teach stats! Had to come back and comment just to make sure you know it :)
Thank you so much! :)
I was struggling to understand Bayes Theorem in my biostats class and this video explained it so well. Thank you Josh!
Thanks!
Only God knows how much I love this channel
bam!
12:35 it’s tricky, meaning the same thing but with different words, thanks Josh for making it clear
Thanks! That was probably the most confusing part of Bayes' Theorem when I learned about it on my own.
This is the best channel for data science!!
Wow, thank you!
if i ever become a professor (this is my dream), i will definitely recommend your videos for my students. your videos helped me sooo much on soooo many courses. currently doing my bachelors, wish me luck! thank you statquest!
Good luck! BAM! :)
Don't know how many ppl relate to it.. but unnecessary optimisations to notations has really screwed up the learning/understanding of concepts. A concrete example is @03:08
This was so spot on!!!
bam!
Greetings from Brazil. You are simply a legend, mr. quest. Love the content and the dorky intros! Thank you for everything! ❤
Muito obrigado!!! :)
Everyone should watch your videos, loved this derivation of the Bayes theorem !
I wish I'd find you sooner.
Thanks!
Right on time 😊 I started studying conditional probability and Bayes' rule just yesterday! Thanks StatQuest!!
bam!
This videos help me a lot to understand the concepts, and then when I read the 'technical' definition I don´t get scared. DOUBLE BAM!
BAM! :)
In most of the cases, what they teach , formulation and nothing else. This video teaches formulation as well as intuition. Great explanation Stat quest. I must say Tripple BAM
Thank you!
Your explanations and animations are simply BRILLIANT 😍
Thank you!
I really like where this is going.
bam!
Quadruple BAM!
I have been waiting for your video on Bayes theorem! So excited :)
bam!
Thank you, Josh! An excellent 'Quest, indeed 😊
Glad you enjoyed it!
unpopular opinion:
i think the redundancy you use in equations are making it more complex,but the explanation is awesome!
Your opinion is actually the popular one, and mine is the unpopular one. Most people write out the equations without the redundancy.
This might be one of the best things on YT.
Wow! Thank you very much! :)
I minor in math in college 20 hrs ago and forgot almost everything. This channel makes everything so simple. Thanks Josh!
Thank you very much! :)
I wish you were my lecturer in school and uni. Your material and presentation is that good 👏
Thank you! 😃
rn studying already really long, but your videos make it doable. Thanks
You got this!
Wow. Thank you so much! I'm not doing anything with Bayesian probability.... but this was so good I had to watch it through! Subbed. I knew how to get a degree of belief and check it... but I didn't know WHY until your video and the "why" is so important.
Glad you enjoyed it!
You are a blessing to humanity!
Thanks!
You are such a great teacher. Any uni should be bending over backwards to put you on tenure track just for the clinical brilliance alone.
Maybe one day!
Dear Josh, your videos are super fun to watch and learn as well as your book, hope you have a chance to revisit the idea of making a series of Bayesian Statistics in 2024. Triple KUDOs to you.
That would be awesome!
Studying philosophy of probability, this vid was a massive help for understanding logical theory type stuff !
Thanks!
U sing really well brings smile on face before setting mood for lecture
Thank you!
At 3:05, I immediately endorsed the video and went to watch the previous video.
BAM!
thank you, Josh! always had a hard time with stats and bayes' theorem
Glad I could help! :)
You are great.
You are really helping build a great future for a lot of people
Thank you! :)
Great video. Struggled to understand this all my life. I am from India and we LOVE soda.
BAM! :)
Hands down best channel❣️
Thank you!
@@statquest I should be the one thanking you, you are absolute legend, literally I am not lying, I have never seen a lecture as clear as this and thank you for this service.
@@hemesh5663 bam! :)
Beautifully explained, finally, I get Bayesian. Bam 💥 💥
Hooray!
find this before my exam, very helpful, wish me luck
Best of luck!
Josh!! Thanks a lot, I love your videos :)
Please show us the hypothesis testing in Bayesian perspective.
Looking forward to more videos with Bayesian statistics.
That's the plan. Unfortunately I have to write a book on machine learning first. However, as soon as finish the book I'll do a whole series on Bayesian statistics.
@@statquest double bam!! Looking forward for your book also
@@statquest Reveal something about your book, please!
@@LQNam I've got about 70 pages done, and in two weeks I'm going to work on it full time until it is done.
My God, this is amazing content! Need more views!
Thank you! :)
I'm not a fan of the "bam" technique; but, in my opinion the explanation is actually clearer, and more easily connected to real-world examples, than other videos which seek to explain Bayes' Theorem. Thank you! (Liked and subscribed. Will be looking at other videos of StatQuest.).
Thank you, but brace yourself for a whole lot of bams! ;)
Today on wards, I am going to be one of your virtual students through out my life...
bam!
@Badrinath SVN - I see what you did there. Clever ;)
Small bam? No. TRIPLE! Thank you for explaining the logic behind Bayesian statistics so clearly.
Hooray! :)
another brilliant insightful tutorial, perfectly memorable!
Thanks!
This is a great intro to the topic!
Thanks!
Hey Josh. This is a great video. You and Brandon Foltz should collaborate. Suggestion: A video applying Bayes Theorem to a continuous dataset would be very interesting.
I hope to do a full series on Bayesian Statistics as soon as I can.
I love you man. you saved my life.
thanks!
You earned a new subscriber!!!🎉🎉🎉🎉 Love your videos!
Edit: Also you earned a new like!
double bam! :)
This is the best explanation on Bayes' Theorem ever!
However, It is so difficult for me to use the kind of basic examples that ilustrates the theorem, for the particular task of analyzing differential gene expression between samples. There are available several "bayesian methods" to analyze gene expression changes and I can not understand the relationship of these methods with the theorem's formulation based on conditional probability.
I am looking forward to see the upcoming videos on the subject. Would you please dedicate some of them to such practical applications of the Bayes' theorem?
Thanks a lot!
Yes, that's the whole idea of Bayesian Statistics and that is what I want to cover in the follow up videos. Unfortunately I have to write a book about Machine Learning first. However, as soon as that is done I'll make the videos about Bayesian Statistics.
I have an exam tomorrow and there is a big chance this is comming back and i didn't completely get is so this really helped :D
Hooray! Good luck with your exam! :)
@@statquest Thank you sir, your videos were a great help to me and other students from my course ^^
@@statquest Good news, i passed :D With a 6/10. There were a few questions in there about random forest and PCA etc, and i was able to clearly explain it thnx to your videos. I now have machine learning and your videos are agian a great help. You channel trully is a gold mine for people who need to learn statistics ^^
@@shillawolf4686 Congratulations!!! Triple BAM!
Unbelievably amazing. Thank you very much for making this channel.
Thank you! :)
Josh, you need to make hoodies and T-Shirts with Statsquach on. It wold be wery funny!
That's a great idea!!! YES!!!! If I do, I'll send you one. BAM! :)
Duuuude, i want one
Sorry, I prefer... TRIPLE BAM!!!
what is this! this is the most clear explanation! thank you!
Glad you think so!
I know this might be a new viewer thing, but BAM definitely makes bayes theorem more clear to me
bam! :)
Excellent video as usual. May I make a wish: would it be possible that you made a video about bayesian vs. frequentist statistics? Every now and then I stumble about this antagonism, but explanations are often kept on a very abstract level. It would be great if you could give a case study which exposed the different (and incompatible?) conclusions these methodologies come to.
Great suggestion! I hope to do it soon.
You are a legend.❤️ from India
Thank you!
I'm looking forward to more Bayesian statistics videos :-)
Me too! Unfortunately I have to write a book on machine learning between now and then....
At 12:50, if both of the conditional probabilities refer to the same yellow area, why are they not equal?
The "left hand" sides of both conditional probabilities refer to the same yellow area, but the right hand sides refer to different areas. "doesn't love candy" refers to the 8 people that don't love candy and "loves soda" refers to the 7 people that love soda.
Great explanation josh 👍👍
Thank you!
Josh, as usual... Great explanation!
Thank you very much! :)
Bam is back
yep! :)
Thanks for showing the omission in the equation.
bam!
Easy to remember and understand. Thank you so much !!!
bam!
Your explanation is easily understandable..thank u so much 👍👍👏👏😁
Thanks!
Very good explanation Josh 👍
Glad you liked it!
Thank you so much for another great video! I couldn't find another video from your channel in which you explain the theory's application. I believe I know it, but when it comes to application, I become very unsure.
Ultimately I'd like to do a whole series of videos on Bayesian methods, but it will still be some time before I can.
@@statquest Thank you so much for your response. Can't wait to watch them and get a better understanding of the topic.
I needed this , hope you talk about natural language processing , I cant find any good resource
I hope to soon.
Statsquatch 😂😂😂boy I have to take my hat off to this guys creativity.
Thanks!
watching this before my stat final... wish me luck!
Good luck! :)
you are the best! now im going to BAM the subscribing button
Wow, thanks!
A box contains 2 fair coins and 1 biased coin. The biased coin has probability of a head as 2/3. A coin is drawn at random from the box and tossed. Then a second coin is drawn at random from the box (without replacing the first one). Given that the first coin has shown head, what is the conditional probability that the second coin is fair?
How would you apply Bayes' rule to solve about problem?
This sounds like a homework problem.
Thank you for your excellent videos. I just have a question about Bayesian Information Criteria. Using the equation BIC = -2*logL + log(numObservations)*numParameters, can BIC be negative? If yes, when comparing different models, should I choose the minimum value? For example, between -9 and -0.5 and +2, and +9 should I choose -9?
Regards,
Nasrin
That's a good question and I'll keep it in mind for when I cover BIC in a separate video.
@@statquest Thank you. The more I read on the internet, the more confused I get.
Great explanation StatQuest 😃. Thanks Josh. Bam!
Glad you enjoyed it!
I LOVE STATQUEST!!!!
Hooray! :)
I LOVE this channel:) THANK YOU!
Glad you enjoy it!
Watching statsquest is like quench your thirst with glucose water. ✌️✌️
bam!
Thank you for this and all of your many spectacular videos. I was wondering if you could do a StatQuickie or other such video on z-scores if you don't already have one available?
I'll keep that in mind.
Me encantan sus videos. Saludos desde Costa Rica!
Muchas gracias!!!
so so clear explanation! subscribed!!!!!
Thanks and welcome!
Waiting for those StatSquatch t-shirts 😁
They are in the works!!!!
Brilliant as always.....
Thank you!
Dude how do you do this? You save my nearly 50 hour of time.
Thanks!
Excellent. Thank for clearing up the P(A!B) should be P(A&B!B). This has confused me for years. I would drop in "given" and replace it with "in". P(A!B) becomes P of getting an A in the domain B.
Noted
I watch your videos just for the fun of it hahaha.... Awesome content!
bam! :)
Thanks for the brilliant Video! It really helps me a lot. :)
Besides, I have a question.
As far as I understand, this Video explains about that Posterior Probability is closed to Likelihood X Prior Probability.
Well I am reading a Paper about VQ-VAE and the author has written on the paper
"the prior is learned rather than static", which I assume that is about prior distribution.
I want to know if there's any difference between prior distribution and prior probability. Or are they the same?
Or am I missing something important?
The prior probability comes from the prior distribution.
Hi Josh.. could you please make a video about sensitivity analysis and it’s uses in non financial settings …
I really appreciate what you do..
How about this? ua-cam.com/video/vP06aMoz4v8/v-deo.html
@@statquest so what's this guy talking about ?
ua-cam.com/video/o6-HCOG1Cp4/v-deo.html
The term "Sensitivity Analysis" has been thrown around by our statisticians a lot ! need to know what they are up to !!