Q22Principles of Artificial Intelligence
Question
Explain Bayesian Networks. Construct a Bayesian network for a medical diagnosis scenario.
Answer
Bayesian Networks are probabilistic graphical models representing variables and their conditional dependencies via a directed acyclic graph.
A Bayesian Network (or Belief Network) is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a Directed Acyclic Graph (DAG). Nodes represent random variables, and directed edges represent causal relationships or conditional dependencies. Each node is associated with a Conditional Probability Table (CPT) that quantifies the effect of the parents on the node.
Bayesian Networks are highly effective for reasoning under uncertainty, allowing for both predictive (forward) reasoning and diagnostic (backward) reasoning.
Medical Diagnosis Scenario:
Consider a simple medical network where 'Smoking' and 'Genetics' are root causes. Both can lead to 'Lung Cancer'. 'Lung Cancer' can subsequently cause 'Fatigue' and a 'Positive X-Ray'.
The DAG would have nodes for S (Smoking), G (Genetics), C (Cancer), F (Fatigue), and X (X-Ray). Edges would be S->C, G->C, C->F, and C->X. Using Bayes' Theorem, if a patient presents with Fatigue and a Positive X-Ray, the network can calculate the updated probability that the patient has Lung Cancer.
