# Probabilistic reasoning in Artificial intelligence

Need of Probabilistic Reasoning in AI

• Unpredictable outcomes
• Predicates are too large to handle
• Unknown error occurs

In probabilistic reasoning, there are two methods to solve difficulties with uncertain knowledge

• Bayes’ rule
• Bayesian Statistics
• Probability: Probability can be defined as chance of occurrence of an uncertain event. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1.

0 ≤ P(X) ≤ 1, where P(X) is the probability of an event X.

• P(X) = 0, indicates total uncertainty in an event X.
• P(X) =1, indicates total certainty in an event X

## Bayesian Belief Network

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. Note: It is used to represent conditional dependencies.

A Bayesian network graph is made up of nodes and Arcs (directed links), where: