I was doing an ASU Masters and shooting myself in the head to understand causality from the Professor Lecture and the sheer ambiguity in it and then I found this gem. Thank You.
It does not have a probability 4. Prof is telling that there are 4 conditional probabilities associated with it...the 4 conditions can be (traffic-jam=1 & late-wake-up=1), (traffic-jam=0 & late-wake-up=0), (traffic-jam=1 & late-wake-up=0), (traffic-jam=0 & late-wake-up=1). So, the associated conditional probabilities will be P("late-for-work=1" | (traffic-jam=1 & late-wake-up=1)) and similarly 3 others.
Please make me correct if I am wrong in my understanding, late meeting and late wakeup are not conditionally independent given the value of late for work
She used to read slides which makes me easy to read them ! Trust me its way better than her explanation 😂😂! If you wanna know why ? See AI videos of her
I was doing an ASU Masters and shooting myself in the head to understand causality from the Professor Lecture and the sheer ambiguity in it and then I found this gem. Thank You.
the best lecture on bayesian network! thank you so much madam!
I did not understand the lecture fully.
Did understand some parts though.
What did you study before for understanding these topics?
Prof Sudeshna Sarkar! This is so wonderful explanation ever on bayesian network, now I see the light.
Thanks prof. So far the best lecture in youtube for bayesian network!!!
Thank you for making me understand the BBN in simple way.
thank you so much madam ,excellent explaination
Thanks a ton ma'am. This will help me in my office project :)
ma'am in belief network diagram if late for meeting then meeting postponed ?
Starts at 3:00
Great lecture
How "late for work" has probability 4 ?
it has two immediate parents hence its probability is affected by 2^2 ways that is 4 ways.
It does not have a probability 4. Prof is telling that there are 4 conditional probabilities associated with it...the 4 conditions can be (traffic-jam=1 & late-wake-up=1), (traffic-jam=0 & late-wake-up=0), (traffic-jam=1 & late-wake-up=0), (traffic-jam=0 & late-wake-up=1). So, the associated conditional probabilities will be P("late-for-work=1" | (traffic-jam=1 & late-wake-up=1)) and similarly 3 others.
Mam is this necessary the variables should be boolean
Please make me correct if I am wrong in my understanding, late meeting and late wakeup are not conditionally independent given the value of late for work
No. At 8:58(& 18:07), professor clearly tells that "given late-for-work", "late-for-meeting" and "woke-up-late" are conditionally independent,
Ma'am, would you be able to give a lecture on python exercise on bayesian network?
She used to read slides which makes me easy to read them ! Trust me its way better than her explanation 😂😂! If you wanna know why ? See AI videos of her
great