Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Book Price in India, Specifications, Reviews & Offers. Buy online at Amazon.

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Very useful, thank you. Is the notebook available?
Thank You for Great Content Explained in Simple Way!
This was very useful! Thank you very much
this video was amazing!! thank you!
Thank you for this video. The material is very good, and it is presented extremely well.
That was super clear explanation for me. Thanks !
Thanks, Great explanation!!!
Thank You is a small word....
How can I used Baysien Network for Machine Learning, and what is the suitable software available for that?
I finally understood the concept of conditional independence, thanks a lot!
Wow... this is amazing. Why cant my university professors teach this clearly? In this age where we do not read textbooks much (and rely on lectures after lectures) there needs to be major improvements in teaching...
please can you give more personal tutorial on how to carry calculations on Bayesian network
Thanks!!!
I still don't understand the difference between enumeration and variable elimination.
great explanation!
Sorry, I am confused the first rule for independence in Bayes nets: "Each variable is conditionally independent of its non-descendents given its parents". Why the non-descendents of a node has to do with its parents?
I have a question about the independence in bayes nets: at 13:14 you say that C is independent of B and D (because of the first rule). at 14:30 you say that a variable is independent of every other variable than its Markov blanket, but there D is included. Does that mean still that C is independant of D because of the first rule or not. I'm a little confused at this point. Anyways great videos, great explanations, thank you very much for creating them.
The course at my university is well given but goes into a lot of details. Your videos help me see the forest through the trees while still being complete and correct. Thank you for the quality content.
Sorry, why doesn't the f function depend on r at 21:50? I mean I know it's in the conditional, but to me that means if r changes the function would change (so yeah its a parameter not a variable but should still be there?)
Excellent way of explaining. Probably can share the tables size reduction with variable elimination to benefit those that is still not familar with computing the table sizes.
This was EXCELLENT !. Thank you
Thanks beyond measurement in money, Bert!
Extremely good. Thanks a ton Bert
Name:- Mayuri Sanjay Sonawane Class:-BE CSE Roll no:-43
Durga Agrawal Roll no :01 Very helpful
Very helpful Samiksha Sushil Sharma 20
Srushti Zope BE CSE 27 Easy to understand
Helpful video👌👌👌 Sneha Kurkure BE/CSE Roll no 10
In the example used, we had the structure of the Bayesian network given and then we could estimate the parameters using BPE. I have read that we can do three things in Bayesian Networks...parameter learning, structure learning and latent structure learning. How do we go about predicting the structure of the Bayesian Network if the parameters are given instead?
Does Probabilistic Graphical Model have some connection with Decision Trees as if we can use Probabilistic Graphical Model to find which data set plays an important role in predicting the output same as in Decision Trees where we find Entropy and Information gain. Are these two somewhere related?
How did you get the value of P(W = T) ?
Is there any way to decrease the correlation by certain design choices of our model Or are these correlations completely determined by the datasets we use?