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Dealing with uncertainty is an inevitable part of real-world problems, and Bayesian Networks offer a powerful framework for reasoning under such conditions.
In this article, we’ll explore the fundamentals of Bayesian Networks and how to implement them using Python.
Bayesian Networks are probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Each node in the graph represents a random variable, and the edges between nodes represent the conditional dependencies among those variables.
One of the key advantages of Bayesian Networks is their ability to handle incomplete data and make predictions based on the available information. This makes them incredibly useful in various domains, such as medical diagnosis, risk assessment, and decision support systems.
To illustrate the concept, let’s consider a simple example: diagnosing a patient’s condition based on their symptoms. We can model this scenario using a Bayesian Network, where the nodes represent the patient’s condition (e.g., flu, cold, or allergy) and the observed symptoms (e.g…
