Demo - Bayesian Network Simulation

Bayesian Network Simulation

Overview

This simulation visualizes a Bayesian Network, a probabilistic graphical model used for decision-making under uncertainty. It consists of nodes (random variables) and edges (dependencies between variables), forming a directed acyclic graph (DAG).

How It Works

• Each node represents an event, such as Cloudy Weather, Rain, or Traffic Jam.

• The edges define causal relationships, e.g., Rain increases the chance of Grass being Wet.

• The simulation calculates probabilities of different events occurring, updating their values dynamically.

• Node colors change based on their probabilities: lighter shades = lower probability, darker shades = higher probability.

• The graph rearranges itself automatically using different layouts, showing how structures adapt dynamically.

Applications

✔ Machine Learning & AI → Used in predictive modeling and reasoning under uncertainty, such as fraud detection and disease diagnosis.
✔ Risk Assessment → Helps industries like finance, insurance, and cybersecurity predict possible outcomes.
✔ Medical Diagnosis → Doctors use Bayesian networks to evaluate symptoms and diseases, improving diagnostic accuracy.
✔ Autonomous Systems → Supports fault detection & predictive maintenance by assessing system health under uncertain conditions.