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Probabilistic Graphical Models (PGMs)
PGMs are a powerful framework for representing and reasoning about uncertainty in complex systems using graphs. They encode conditional dependencies between variables, allowing for inference, learning, and prediction when data is incomplete or noisy. Examples include Bayesian networks and Markov random fields, which are foundational for many AI applications involving uncertain knowledge.
In plain terms
Think of it as a detailed family tree that shows not just who is related, but also how strongly their traits might influence each other.
Why it matters
PGMs enable AI to handle real-world uncertainty gracefully, leading to more robust and informed decisions.
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