Question

Bayesian Network is a graphical model that efficiently encodes the joint probability distribution for a large set of variables is it true or false?

Answer 1

Probabilistic graphical models are graphs in which nodes represent random variables, and the (lack of) arcs represent conditional independence assumptions. Hence they provide a compact representation of joint probability distributions.Undirected graphical models, also called Markov Random Fields (MRFs) or Markov networks, have a simple definition of independence: two (sets of) nodes A and B are conditionally independent given a third set, C, if all paths between the nodes in A and B are separated by a node in C. By contrast, directed graphical models also called Bayesian Networks or Belief Networks (BNs), have a more complicated notion of independence, which takes into account the directionality of the arcs, as we explain below.

Answer 2

Q. Bayesian Network is a graphical model that efficiently encodes the joint probability distribution for a large set of variables.

(a) True

(b) False

Answer : True

Extra information :

-- Bayesian Network (BN) is a graphical model or structures that efficiently encodes the joint probability distribution for a large set of variables or in other words, it is a type of graphical model which is used to represent the probabilistic relationship or conditional dependencies among a set of random variables.

-- It is also called Belief Networks or Bayes Nets.