# Unit 3 12 General Bayes Networks [Thrun] So we’re now ready to define Bayes networks in a more general way. Bayes networks define probability distributions over graphs or random variables. Here is an example graph of 5 variables, and this Bayes network defines the distribution over those 5 random variables. Instead of enumerating all possibilities of combinations of these 5 random variables, the Bayes network is defined by probability distributions that are inherent to each individual node. For node A and B, we just have a distribution P of A and P of B because A and B have no incoming arcs. C is a conditional distribution conditioned on A and B. D and E are conditioned on C. The joint probability represented by a Bayes network is the product of various Bayes network probabilities that are defined over individual nodes where each node’s probability is only conditioned on the incoming arcs. So A has no incoming arc; therefore, we just want it P of A. C has 2 incoming arcs, so we define the probability of C conditioned on A and B. And D and E have 1 incoming arc that’s shown over here. The definition of this joint distribution by using the following factors has one really big advantage. Whereas the joint distribution over any 5 variables requires 2 to the 5 minus 1, which is 31 probability values, the Bayes network over here only requires 10 such values. P of A is one value, for which we can derive P of not A. Same for P of B. P of C given A B is derived by a distribution over C conditioned on any combination of A and B, of which there are 4 of A and B as binary. P of D given C is 2 parameters for P of D given C and P of D given not C. And the same is true for P of E given C. So if you add those up, you get 10 parameters in total. So the compactness of the Bayes network leads to a representation that scales significantly better to large networks than the common natorial approach which goes through all combinations of variable values. That is a key advantage of Bayes networks, and that is the reason why Bayes networks are being used so extensively for all kinds of problems. So here is a quiz. How many probability values are required to specify this Bayes network? Please put your answer in the following box. ## 13 thoughts on “Unit 3 12 General Bayes Networks”

1. waluntube says:

This video should be at the begining of the introduction to Bayes Networks, not at the end!

2. Ammar Jabakji says:

thanks

3. steve callender says:

you see how it is (2^5) -1 yeah? how come there is a -1 and its not just 2^5. don't really understand…

4. xxx309028989s says:

Is this part of a course? Where is the course overview?

5. John Adams says:

Yes, there are hundreds of parts of this course, but the channel didn't organize trhought a playlist. There are user playlist. Type "Unit 0w". Warning, is really very hard, much much math.

6. Logeswaran Nagaradjane says:

I think he counting from 0..
so he subract 1 from total

7. Manan Gandhi says:

I think he is wrong,,
for n nodes, you will have 2^n combinations…
Check Wiki example here..
http://en.wikipedia.org/wiki/Joint_probability_distribution#Example

Correct me if I am wrong…

8. Muhammad Usman Jamil says:

Probabity Values: 2^6-1=63
Joint Probability: 13

9. Sachleen Kaur says:

what is the correct ans ?? please??

10. resit k says:

do we include parameter into the equation?

11. Jatin Sethi says:

I think it is 11. But Ms. D thinks it is 13. We both are stupid, btw

12. Shreya Saxena says:

Total probability values = 63
Using Joint Probability Distribution: 13 values

13. André Mariano says:

what would be the probability of B/C for example?