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Prior Distribution

In Bayesian methods, the initial belief about parameters before observing data.

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  1. Bayesian Inference ✓ understood

    Using Bayes' theorem to update beliefs about parameters given data, incorporating uncertainty.

  2. Prior Distribution · you are here ✓ understood

In Bayesian methods, the initial belief about parameters before observing data.

This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.

  • Bayesian Inference
  • Posterior
  • MAP

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Prior Distribution

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Conjugate Prior Maximum A Posteriori

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Foundations Posterior Distribution In Bayesian methods, the updated belief about parameters after observing data. Foundations Maximum A Posteriori Parameter estimation that incorporates prior beliefs, maximizing posterior probability rather than just likelihood. Foundations Conjugate Prior A prior that when combined with a likelihood results in a posterior of the same family, simplifying Bayesian inference. Training Regularization Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping). Foundations Inductive Bias Assumptions built into a learning algorithm that guide it toward certain solutions over others.