Bayesian Networks have their origin in Bayes Theorem, which is named after Thomas Bayes, the famous British mathematician. This theorem is essentially a mathematical formula used to determine conditional probability. Bayesian Networks in the field of artificial intelligence is derived from Bayesian Statistics, which has Bayes Theorem as its foundational layer. A Bayesian Network consists of two modules – conditional probability in the quantitative module and directed acyclic graph in its qualitative module. In AI and machine learning, Bayesian Networks are tools used for reasoning and modeling based on uncertain beliefs.