Thank you! This is very helpful having all the exact calculations and also going over all the relevant formulas up front is super. The calculations of the probabilities across the probability tables went a bit fast for me but I'll break it down and study it tomorrow. This is the only full example I found after scouring the internet, looking in tesctbooks, and reviewing the lecture videos for tge course Im in.
Hi there, I like your matrix solution so much that make the computation so easy, I also calculate the same question in Naive bayesian Classifier, thich give me an astonishingly close result, which is 0.28461. I want to investigate more on the reasons that easier architecture can achieve a similar accuracy as a more sophisticated architecture, do you or anyone who saw this comments have any ideas?
@@froylanm.wbariomartinez470 It is either you have historical data that you can extract the probabilities from by dividing the number of times a specific event happens over the total number of events, or, in case you do not have data, you can ask experts in a specific field to provide their assessment of the probabilities. Read about (Expert Judgement).
This video is saving my life right now
Thank you! This is very helpful having all the exact calculations and also going over all the relevant formulas up front is super. The calculations of the probabilities across the probability tables went a bit fast for me but I'll break it down and study it tomorrow. This is the only full example I found after scouring the internet, looking in tesctbooks, and reviewing the lecture videos for tge course Im in.
ua-cam.com/video/m4l6U1vTNKY/v-deo.html
Thanks a lot, I was looking for this all over the Internet for weeks. ❤
you are a life savor. Thanks
FINALLY! Thank you
Thanks! this is very useful
excellent video on bayes nets. thank you
Amazing Video !
Wow co video John, very cool!
ua-cam.com/video/m4l6U1vTNKY/v-deo.html
Thank you so much🎉🎉🎉
This video is loaded. do you have another one where you go into details?
Hi there, I like your matrix solution so much that make the computation so easy, I also calculate the same question in Naive bayesian Classifier, thich give me an astonishingly close result, which is 0.28461. I want to investigate more on the reasons that easier architecture can achieve a similar accuracy as a more sophisticated architecture, do you or anyone who saw this comments have any ideas?
mates! What's the capital letters instead of lowercase letters mean? eg: P(a) turning into P(A)?
For anyone still confused by this: P(A) is the total probability distribution of A, which can then turn into two distinct outcomes P(a) and P( ¬ a)
why don't you have other videos
But how to define the values of probabilities used in this example or in any other example?
Excuse me, do you know how to define it now ? thanks
@@froylanm.wbariomartinez470 It is either you have historical data that you can extract the probabilities from by dividing the number of times a specific event happens over the total number of events, or, in case you do not have data, you can ask experts in a specific field to provide their assessment of the probabilities. Read about (Expert Judgement).
@@bmyalbara2 Thank you so much
why have you ignored alpha in your computation?
you can just normalize in the end to account for alpha
woah is this brad pitt?
Really helpful, thank you