Bayesian Network
2024 | AI Dictionary
What is a Bayesian Network: A probabilistic graphical model using directed graphs to represent variables and their dependencies for reasoning.
What is a Bayesian Network?
A Bayesian Network is a probabilistic graphical model that represents a set of variables and their conditional dependencies through a directed acyclic graph. It uses Bayes’ theorem to compute probabilities, making it useful for reasoning under uncertainty.
Types of Bayesian Networks
- Dynamic Bayesian Network: Models sequences of variables over time, often used in time-series data.
- Continuous Bayesian Network: Works with continuous variables, extending traditional Bayesian Networks to non-discrete data.
- Discrete Bayesian Network: Deals with categorical or discrete variables, commonly used in classification tasks.
Applications of Bayesian Networks
- Medical Diagnosis: Assesses the probability of diseases based on symptoms and patient history.
- Risk Assessment: Evaluates potential risks and outcomes in fields like finance and engineering.
- Natural Language Processing (NLP) : Helps in tasks like part-of-speech tagging by predicting word categories based on context.
Example of a Bayesian Network
An example of a Bayesian Network is in medical diagnosis systems, where it estimates the probability of a disease given observed symptoms, aiding doctors in making data-driven diagnoses.
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