Bayesian Networks: Inference using Variable Elimination

Рет қаралды 33,016

IIT Delhi July 2018

4 жыл бұрын

Пікірлер: 16

@towers3372 4 ай бұрын

I love this video, it's much better than the mechanical approach to Variable Elimination. Now I understand WHY it works, and it is so much easier to remember!

@Sergeak21 8 ай бұрын

i wish you the very best, with all the love - a struggling student

@aisha3540 2 жыл бұрын

very helpful explanation! i wish my professor explained it this well :)

@ashwinkashyap8665 2 ай бұрын

Excellent explanation professor!

@shikharpandya4927 Ай бұрын

Thanks a lot IIT D

@suryakanth5370 2 жыл бұрын

This is what should be captioned as watch till end

@raihanulbaritanvir3845 3 жыл бұрын

awesome lecture

@VSSRaviTejaDendukuri 27 күн бұрын

From 14:00 How was sum over E written as f1(A,B) and sum over B written as f2(A)?

@gijsvermeulen8235 4 ай бұрын

Love the intro

@akash1927 3 жыл бұрын

I don't understand how that full joint distribution summing over hidden variable came. EE Dept.

@kpb6 3 жыл бұрын

@Akash We have assumed that Earthquake and Burglary are two independent events and they influence Alarm. Hence P(B) and P(E) are multiplied as they are. But alarm ringing depends on E and B hence P(A|,E.B). Since alarm influences John calling or Mary calling, we have P(j|A) and P(m|A). Hence P(B) P(E) P(A|E,B) P(m|A) P(j|A)

@manjunathakapilsharma 3 жыл бұрын

@@kpb6 I have one more doubt , what if we have some more parents to earthquake and burglary and some more child nodes to john and mary ?? Do all these things should be considered as hidden variables for computations?

@joebashour 3 жыл бұрын

@@manjunathakapilsharma Precisely. For instance: if [Earthquake] had 2 causal nodes (i.e. parents), then we would do P(E | parents) etc.

@9181shreyasbhatt 4 ай бұрын

If u can mention the particular timestamp, we might be able to exactly clarify the doubt.

@shreerambhat1442 4 ай бұрын

tq so much 😇@@kpb6

@nealpobrien Ай бұрын

Good lecture, but he expects students to guess what he's about to say often without it yet being clear what he's looking for, which seems common in teaching probability.