Bayes network

(CentreforKnowledgeTransfer)
institute
 Bayesian belief network is key computer technology for dealing with probabilistic
events and to solve a problem which has uncertainty. We can define a Bayesian
network as:
 "A Bayesian network is a probabilistic graphical model which represents a set of
variables and their conditional dependencies using a directed acyclic graph."
 It is also called a Bayes network, belief network, decision network, or Bayesian
model.

institute
 Bayesian networks are probabilistic, because these networks are built from a
probability distribution, and also use probability theory for prediction and
anomaly detection.
 Real world applications are probabilistic in nature, and to represent the
relationship between multiple events, we need a Bayesian network.
 It can also be used in various tasks including prediction, anomaly detection,
diagnostics, automated insight, reasoning, time series prediction, and decision
making under uncertainty.

institute
 Bayesian Network can be used for building models from data and experts
opinions, and it consists of two parts:
 Directed Acyclic Graph
 Table of conditional probabilities
 The generalized form of Bayesian network that represents and solve decision
problems under uncertain knowledge is known as an Influence diagram.

institute
 A Bayesian network graph is made up of nodes and Arcs (directed links), where:
• Each node corresponds to the random variables, and a
variable can be continuous or discrete.
• Arc or directed arrows represent the causal relationship
or conditional probabilities between random variables.
These directed links or arrows connect the pair of nodes in
the graph.
These links represent that one node directly influence the
other node, and if there is no directed link that means that
nodes are independent with each other
• In the above diagram, A, B, C, and D are random variables
represented by the nodes of the network graph.
• If we are considering node B, which is connected with node A by a
directed arrow, then node A is called the parent of Node B.
• Node C is independent of node A.

institute
The Bayesian network has mainly two components:
 Causal Component
 Actual numbers
 Each node in the Bayesian network has condition probability distribution P(Xi
|Parent(Xi) ), which determines the effect of the parent on that node.
https://www.youtube.com/watch?v=480a_2jRdK0

institute
 Bayesian network is based on Joint probability distribution and conditional
probability. So let's first understand the joint probability distribution:
 If we have variables x1, x2, x3,....., xn, then the probabilities of a different
combination of x1, x2, x3.. xn, are known as Joint probability distribution.
P[x1, x2, x3,....., xn], it can be written as the following way in terms of the joint probability distribution.
= P[x1| x2, x3,....., xn]P[x2, x3,....., xn]
= P[x1| x2, x3,....., xn]P[x2|x3,....., xn]....P[xn-1|xn]P[xn].
In general for each variable Xi, we can write the equation as:
P(Xi|Xi-1,........., X1) = P(Xi |Parents(Xi ))

institute
 Vicky installed a new burglar alarm at his home to detect burglary.
 The alarm reliably responds at detecting a burglary but also responds for minor
earthquakes.
 Vicky has two neighbors Akil and Reena, who have taken a responsibility to
inform Vicky at work when they hear the alarm.
 Akil always calls Vicky when he hears the alarm, but sometimes he got confused
with the phone ringing and calls at that time too.
 On the other hand, Reena likes to listen to high music, so sometimes she misses to
hear the alarm.
 Here we would like to compute the probability of Burglary Alarm.

institute
Calculate the probability that alarm has sounded, but there is neither a
burglary, nor an earthquake occurred, and Akil and Reena both called the Vicky.
Solution
 The Bayesian network for the above problem is given below. The network
structure is showing that burglary and earthquake is the parent node of the
alarm and directly affecting the probability of alarm's going off, but Akil and
Reena's calls depend on alarm probability.
 The network is representing that our assumptions do not directly perceive the
burglary and also do not notice the minor earthquake, and they also not confer
before calling.
 The conditional distributions for each node are given as conditional
probabilities table or CPT.
 Each row in the CPT must be sum to 1 because all the entries in the table
represent an exhaustive set of cases for the variable.
 In CPT, a boolean variable with k boolean parents contains 2K probabilities.

institute
List of all events occurring in this network:
 Burglary (B)
 Earthquake(E)
 Alarm(A)
 Akil Calls(D)
 Reena calls(S)

institute
 P(B= True) = 0.002, which is the probability of burglary.
 P(B= False)= 0.998, which is the probability of no burglary.
 P(E= True)= 0.001, which is the probability of a minor earthquake
 P(E= False)= 0.999, Which is the probability that an earthquake not occurred.

institute
 We can provide the conditional probabilities as per the below tables:
 The Conditional probability of Alarm A depends on Burglar and earthquake:
B E
P(A=
True)
P(A= False)
True True 0.94 0.06
True False 0.95 0.04
Fals
e
True 0.31 0.69
Fals
e
False 0.001 0.999

institute
A
P(D=
True)
P(D= False)
True 0.91 0.09
Fals
e
0.05 0.95
A P(S= True) P(S= False)
True 0.75 0.25
False 0.02 0.98
Conditional probability table for Akil Calls:
The Conditional probability of Akil that he will call depends on the probability of Alarm.
Conditional probability table for Reena Calls:
The Conditional probability of Reena that she calls is depending on its Parent Node "Alarm."
From the formula of joint distribution, we can write the problem statement in the form of probability
distribution:
P(S, D, A, ¬B, ¬E) = P (S|A) *P (D|A)*P (A|¬B ^ ¬E) *P (¬B) *P (¬E).
= 0.75* 0.91* 0.001* 0.998*0.999
= 0.00068045.

institute
Hence, a Bayesian network can answer any query about the
domain by using Joint distribution.
The semantics of Bayesian Network:
There are two ways to understand the semantics of the Bayesian
network, which is given below:
1. To understand the network as the representation of the Joint
probability distribution. It is helpful to understand how to
construct the network.
2. To understand the network as an encoding of a collection of
conditional independence statements. It is helpful in
designing inference procedure.

Bayes network

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