Bayesian Network

[@lipsachhotray1021]

the best lecture on bayesian network! thank you so much madam!

[@umang9997]

I did not understand the lecture fully. Did understand some parts though. What did you study before for understanding these topics?

[@dibyendurb]

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.

[@tsitsosephapo5880]

Prof Sudeshna Sarkar! This is so wonderful explanation ever on bayesian network, now I see the light.

[@mdtanvirrahman6801]

Thanks prof. So far the best lecture in youtube for bayesian network!!!

[@JC-nx5xx]

Thank you for making me understand the BBN in simple way.

[@itneka]

Thanks a ton ma'am. This will help me in my office project :)

[@South_UPSC_Wallah]

ma'am in belief network diagram if late for meeting then meeting postponed ?

[@shubhammishra4565]

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

[@abhijeetsharma5715]

No. At 8:58(& 18:07), professor clearly tells that "given late-for-work", "late-for-meeting" and "woke-up-late" are conditionally independent,

[@sanchitsharma7472]

thank you so much madam ,excellent explaination

[@statisticsforbeginners6841]

Mam is this necessary the variables should be boolean

[@tolifeandlearning3919]

Great lecture

[@rekhakushwaha7224]

How "late for work" has probability 4 ?

[@lipsachhotray1021]

it has two immediate parents hence its probability is affected by 2^2 ways that is 4 ways.

[@abhijeetsharma5715]

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.

[@harshvardhan184]

Ma'am, would you be able to give a lecture on python exercise on bayesian network?

[@ramum5424]

Starts at 3:00

[@atreyatata982]

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

[@AjazAnsari-zl9yp]

great