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Bayesian Inference

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

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

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

  • MAP
  • Prior
  • Posterior
  • Probability

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Bayesian Inference

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Gaussian Process Markov Chain Monte Carlo Posterior Distribution Prior Distribution Variational Inference

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Foundations Maximum Likelihood Estimation Finding model parameters that maximize the probability of observing the training data. Foundations Maximum A Posteriori Parameter estimation that incorporates prior beliefs, maximizing posterior probability rather than just likelihood. Foundations Markov Chain Monte Carlo Sampling methods for approximating distributions, especially for Bayesian inference in complex models. Foundations Variational Inference Approximating complex distributions by optimizing over a simpler family, an alternative to MCMC. Foundations Gaussian Process A non-parametric Bayesian approach for regression and classification, defining distributions over functions. Foundations Prior Distribution In Bayesian methods, the initial belief about parameters before observing data.