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Now I get the theorem thanks a lot

I'm a new comer for coding and love to play around. After watching your excellent video, I got an idea about finding the path with maximum score. I got screwed up many time and along the way I learn your logic of your code. Then I finally got it by modifying your code. The first table, I change every cell with value of 100 to -10. On Solver I change Objective to Maximize. My Variable range is the same as your. My Constraints I put all the values of Total Out and Total in to be equal or less than zero (<= 0) And the rest are the same as your code. Got Total distance = 12 , from 1 to 3, to 5 , to 4 , then to 6. Thank for inspiring me.

Your explanation is very confusing...go and check how @organic chemistry tutor presents

Working ❤ thanks

extraordinary explanation mam...thank you.

The presentation is appreciated but the calculation in the first question is way too off.. still good efforts!

The second question confusing!! We don’t even know what we are being ask to look for?? You just started answering

really nice way to teach i love it

Thank you very much. Your videos are very detailed and easy to understand. It's broadening my understanding in Data Analysis. I will be grateful if I can have your email and send your a mail. I would love to become an expert in using the Excel and any other statistical tool for data analysis. Thank you

Using Baye’s theorem: P(M|SE) = P(SE|M) * P(M) / (P(SE|M)*P(M) + P(SE|F)* P(F)) --- (1) = 5%*60%/(5%*60% + 4%* 10%) = 3%/7% = 0.42857 or 42.86% is the probability that someone is male given that someone is showing a side effect. Let’s break it down: What % of overall population shows side effect? That is 10% of 40% females = 4% and 5% of 60% males = 3%. So this is total 7%. This is what goes in the denominator of (1) above: P(SE) = P(SE|M)* P(M) + P(SE|F)* P(F). Allow me to take another shot at explaining this without losing our heads in all the formulas. ‘ You have a set of males and females (in a gender binary world). 40% females and 60% males. Of the 40% females 10% have SE. So how many females with SE? 10% of 40% = 4% Similarly how many males with SE? It is given 60% of population is males and 5% of them have SE so 5% * 60% = 3%. So right there you have 3% + 4% = 7% of the overall population that’s showing SE. How many of them is males? We know already that is 5% * 60% = 3%. So of the 7% actually 3% are males. So that’s 3% / 7% or 42.86% is the answer That 3% + 4% is actually the denominator P(SE|M) * P(M) + P(SE|F) * P(F) And 3% is simply P(SE|M) * P(M)

Thanks for this very helpful lesson. Please make more videos on probability. Your explanations are easy to understand

The answer is 42.85% probability not 46.15%.

I love the way u teach ma'am 🥹. I understand everything. Much love ❤️

How did you come up with constraint 1,0,0,0,0,-1? 6:09

Hello. Thank you for putting up your shortest path solution. I am trying to do something similar-but-more complex, and am having trouble, and I was wondering if you could give me some guidance. What I want to in Excel with shortest paths is: 1) layout a large m-by-n matrix of nodes, with distances in meters between them. I'll use pseudo-chess-board nomenclature with one axis being A-Z and one axis being numbered 1-n (calling nodes "A1", "C3", "F7", etc) 2) have the ability to request multiple shortest paths from (say) B3->F8, G2->A14, F2->R23, etc 3) partially congest a route based on previous paths. For example, if a route is found it may be tagged as 25% congested between two nodes. Another route may add to this. Eventually the route would be congested, and an alternative shortest path would have to be found. 4) ideally I'd like to make it iteratively optimise, but I realise that may be impossible to do in Excel, so the above congestion may have be sequentially built in Do you know of any examples where such a thing has been done? Thank you in advance, Adam

Thank you great video

Excellent explanation from first principles that is much better than most, which treat Bayes as a black box formula. Good also that the answer is scientifically accurate wrt gender. Imagine the contortions necessary if sex was arbitrary and based on self-identification? Statistics would be meaningless if sex were treated as a fiction.

Hello Doulton.. Hope you are doing great.. I saw your Statistics videos and each and every explanation was awesome.. Teaching is a skill which everyone can not get it.. You have that naturally.. It will be great if you post more and more on Statistics and other subjects what you know. Thanks for your wonderful knowledge sharing :)

Very Useful

BTW ATTENTION: THE PROBABILITY OF P(SE) IS NOT 0.065 BUT 0.07!

"Just an example, not real life at all", lmao! You're totally delusional, lady. Or should I say, "dude"?

This was really helpful.

done

I think there is a problem on the second example, the first tree diagram, the weak part (percentages of indicating and not indicating)

its always the people who have a pen and paper setup that will save your life thank you very much

nice

NOT WORKING

positive should be 90% and negative should be 10% for the market question

Nice video, but it will not work for some other examples. I tried this mothed several times. but the result is wrong

I like your strategy ❤️ thank you!

If the residuals are not normally distributed, then is the predictions < target or predictions > target ?

Wonderful presentation. I have understood well the Bayes' Theorem

Wow....! Great explanation. Please keep it up.

Node 0: Market State Edge 0.0 Strong: 70% → Node 0.0 Research Result → Edge 0.0.0 Positive: 80% → Edge 0.0.1 Negative: 20% Edge 0.1 Weak: 30% → Node 0.1 Research Result → Edge 0.1.0 Positive: 10% → Edge 0.1.1 Negative: 90% The probability for a positive result: P(+) Is the combination of the branches that contain “positive” in the original tree. So in other words: Edge 0.0 Strong: 70% → Edge 0.0.0 Positive: 80% and Edge 0.1 Weak: 30% → Edge 0.1.0 Positive: 10% Applying the denominator of Bayes' Theorem: Positive: P(A|B)P(B) + P(A|!B)P(!B) ⇒ P(+|S)P(S) + P(+|W)P(W) ⇒ P(0.8)P(0.7) + P(0.1)P(0.3) ⇒ (0.8)(0.7)+(0.1)(0.3) ⇒ 0.59 = 59% And thus we automatically know the answer to negative being 41%. P(S|+) = P(+|S)P(S) / P(+) ⇒ P(S|+) = P(0.8)P(0.7) / P(0.59) ⇒ P(S|+) = (0.8)(0.7) / (0.59) ⇒ 0.949152542 ≈ 0.95 P(S|-) = P(-|S)P(S) / P(-) ⇒ P(S|-) = P(0.2)P(0.7) / P(0.41) ⇒ P(S|-) = (0.2)(0.7) / (0.41) ⇒ 0.341463415 ≈ 0.34 Node 0: Research Result Edge 0.0: Positive: 59% → Node 0.0 Market State → Edge 0.0.0 Strong: 95% → Edge 0.0.1 Weak: 5% Edge 0.1: Negative: 41% → Node 0.1 Market State → Edge 0.1.0 Strong: 34% → Edge 0.1.1 Weak: 66%

A' = nought A or not A? Or nawt A??? With programming we'd say either !A or A = 0 (not A and naught A respectively). Edit: Nevermind. I now see that it's "Not A" ^^

I love this video😍. By far the best explanation of how to figure out Bayesian’ theorem.

Thanks

how do it get to you questions pls send your facebook name or Email account

P(a tutorial from you is awesome)=1

Your P(SE) should be .07, not .065. The final answer is 3/7 = .429

Indeed, this is a great help to teachers of Mathematics.

In your first example (0.1x0.4) + (0.05x0.6) = 0.07 not 0.065. In your second example (0.2x0.7)/0.41 = 0.3415 not what you calculate

YOU ARE A HERO not a TEACHER MISS XD

I want to thank the author and also Google developers who made it possible for me to stumble upon this video and learned the new intuition about how the Bayes` Theorem works. I even watched 3Blue1Brown`s excellent visualization, but still didn't feel confident about ACTUAL understanding of this relationship between probabilities. To be honest, it scares me to think about how many of these gems are buried deep on the Internet and the chances to find them are only getting smaller.

Hi, Thank you very much. This helped me resolving my doubts.. Aashay S. (India 🇮🇳 )

Thank you so much for this Doulton! You're a rockstar!!!!

Nice thanks for the session :)

thank you

Well done you have a real knack for that.

really helpful, thank you so much for this amazing presentation