Bayes' Theorem EXPLAINED with Examples 

I don't know how this is not the most viewed Bayes' theorem video because its the most helpful in youtube

Probably the clearest explanation of Bayes Theorem I have seen so far. Beautifully done. Got to watch all your videos now.

Thanks a lot for this intuitive example. It helped me a lot to understand this mechanism when I understood that as p(cloudy) becomes smaller, p(rain|cloudy) becomes greater, all else being equal. Since p(cloudy) is the numerator. This makes sense intuitively, because in a situation clouds are rare (i.e. p(cloudy) is smaller), but when it rains, there were often clouds in the morning (i.e. p(cloudy|rain) is large), the prediction value of it being cloudy in the morning is high. Reversely, in a climate where it is always cloudy (i.e. p(cloudy) is near one), the fact that it's cloudy in the morning does not tell you much in terms of how much rain you will get.

The best explanation of Bayes' theorem on youtube, thank you

Saying this video is the best is an understatement. Thank you so much for posting this beyond-amazing video!

That was easy and simple to understand. Master it is another thing, but my guess is that you saved me some precious time with this video. Thank you a lot.

Please make more videos on the probabilities. Thank you so much We appreciate your effort.

This is so beautiful. I didn't understand it really at first, but after now I have a pretty great idea of it

Great videos Mark, you inspire us every day with your slogan "You've big dreams, don't let a class get in your way." The likelihood of it being rain while picnic seems low, as it's less than 0.5 / 50%. So I think.i would still go on the picnic.

superb video. How easily the theorem is explained with the help an excellent example...thanks.

Got an exam in 8 min, this was good help

i feel like i want to cry

I think the example in this video is better than what 3B1B gave in his Bayes' theorem video. The starting wasn't good because you just spammed the formula but the example and the way you conveyed it is really good. One can understand the principle through your example. Good work!

This was very helpful am taking statistics class and was so lost. Thanks

Please keep making more videos. I am an MPH student at Harvard, and you make the concepts extremely understable. Sending you a lot of love

Thank you so much. It took 1 video of you understand 3 hours of lecture.

Thanks for your videos. It helped a lot. Please do something on hypothesis. Thanks

Thank you so much for this vid man, your method of explanation was impressive

Amazing video, thank you for the explanation it finally clicked.

Thank you soo much for well explaining this concept. I now can say that I understand it better!

Very precisely explained.Thank you Sir❤

Well done! Excellent Explanation. And I would definitely cancel the picnic I'm worry wart. LOL

Thanks for the concept ☺️ I think I will try to reschedule 😅❤

honestly u did better job than many others

God bless you sir for this video. I HAVE went through few videos on RU-vid and this was one of the best where my mind has understood this fully. Now lets see if you have stuff on Binomial distribution. Thanks just subscribed now

This was super helpful thank you!

Really helpful man thank you

Thanks! Very helpfull and understandable.

Very nicely explained

Super helpful ,Thank you 🌹

Thank you for creating this theorem, Bae 😍

THANKS MAN! Tomorrow is my official school graduation exam and honestly i didn't ecen know a word about this concept so i was worried and your video popped up! Thanks a bunch for making me understand it! Ill be back to report my marks if i am reminded of this comment! P. S: keep doing this. We love it and will support you through the best of our efforts!

English is not my first language and you still made it very easy :D

Good explanation.Thank you

love your work man, keep up the good work!

I’ve never seen a more clear explanation of how Bayes’ Theorem can be applied. This is extremely helpful! Thank you so much!

THANK YOU! I was trying to learn Bayes' Theorem off the example of "Go For Broke" the gameshow. 🥵 my brain was twisting in on itself. THIS I can understand.

That's so helpful ❤🤝

Omg thank you sir thank you so much ❤ the way u explained it,, cleared my all doubts regarding this topic ❤

Super explanation. Thanks Sir

Great explaination sir

this was great thnx for the example

great, great and best explanation

Thank you so very much!

This is very helpful❤

Thank you NRI ❤

Amazing video!! thank you:))

Super helpful and straightforward. Thank you sir please keep posting

Great videos man they help a lot

How you will connect prior and posterior terms with this?

Good explanation 🎉

Great video thanks

this helped tonnnnnnn thankyou

It indeed helped ! appreciate it gentleman

Thank you. I'm still having concept issues. As a teaching technique is it possible for you to summarize the meaning of the numerator and what the denominator accomplishes in the equation

Thanks 👍👍

Thank you so much

Pretty helpful!

Thank you!

So farthis is the best bayes explanation. Can you explain this thing using a venn diagram and a probability distribution for the cloudy rain example

that was so helpful, thanks

This series is amazing. Must have been hard to make these beautiful animations. Thank you so much❤. Can you please make two more distributions viz: 1. "Poisson Distribution" and 2. "Exponential Distribution" And explain the intuition behind the mean and standard deviation in these distributions like you did in Uniform distribution video?

Damn, perfectly explained. Thanksssssssssss!

hi I want to ask so in this case what the addictional knowledge? the probability of beibg cloud?

great vids bro

Which software are you using to make these videos?

Nice explain dear

Hi, so is conditional probability used with limited information in a question, however Bayes theorem can be used to answer a question that has more information? I'm just struggling with which one to use in an exam question

Excellent

What an example ❤❤

Can we solve the same example by conditional probability?

It's awesome 🎉😢

Thankyou!

you really aced it

I would have liked if you incorporated dark clouds vs white clouds in the calculation.

I am from Bangladesh, have started to learn machine learning. For which I have to learn probability and statistics. I have clear out all the topics on probability by 11-12th books. But BAYES' THEOREM was seemed to tough to understand. So I came to RU-vid and saw lots of videos which was even approximately half an hour! Although they tried for a long time, they all were gloomy to understand. But your 8 minute video is so effective than all those videos. Thanks. I have subscribed your channel. I will visit again if any other topics I have to understand in future.

nice example

it would make me 48% worried about rain and 48% considerable of postponing the picnic

in need more explanation of this "give" thing

I LOVE YOU GUYS

That's great when the example gives what P A|B is

Wow fairly good explanation. i just understand it perfectly today. However, in the process, i found another better way to comprehend this theorem. To those who still do not understand. Read this. First you must understand what p(a/b) is. It is the probability that a happening when we already know that b happened. To find p(a/b) we need to find prob that a and b happening at the same time , and divided it by prob of b happening. To find prob that and b happening at the same time (p (a interect b)), you can find that indirectly from prob that b happening when we already know that a happened multiplied by prob of a happening Ah.... i need a pen and a paper to convey this concept 🙄

NICE VIDEO

Do you use the Python library called "manim" to create these beautiful animations for your great videos.

I think the most difficult part overall regarding to probability problems... are the wordings. They seem to be confusing

Thus is amazing

Genius!

Can we please have a video about P value and what does it mean?? pretty please

Nice!

Depends on how much you like picnics, how often you want to go on picnics, if you think rain ruins it and maybe you might already think a cloudy day isn't nice for picnics. But before we think about that, let's think about the ethics of holding a picnic and it's core components. We NEED to apply divide and conqure on this problem before we could even start to make a decision. We might even need to apply derivatives to calculate the slope at how long the picnic takes (x) and how much fun it is (y). Then we can decide the optimal time to hold the picnic 🙊

thankkkkkkkkkkkk youuuuuuuuuuuuuuu !

if one knows that there are 12 rainy cloudy days (the 80% of 15) in 100 days and 25 cloudy days in total why one can’t just calculate 12/25= 0.48, without all the machinery and the language that the Bayes formula brings along? Is there something wrong in just applying the definition of probability?

How did you made your subscribe button glow at 0:25 ??

I'm still puzzeld on which data is A and which is B - and why. Swapping things around changes the outcome of the formula, doesn't it?

You explain very well but it would be more helpful if you broke down how to determine step by step which is a and which is B.

Dude, when the guy says hit the subscribe button, the button lights up. I noticed it just now

On the last slide with example it should be “when it rains”, not where.

So what you’re saying (if I pause the video @6:23) is: If the probability of it being cloudy outside is 4 times greater if it also rains that day, then the probability of it raining on any given day is also 4 times greater if it happens to be a cloudy day?

Definitely reschedule the picnic

Ive seen bayes theorem be written as P(Ei|A) = P(Ei)P(A|Ei) / ∑ P(Ek)P(A|Ek) can you explain this version?