Definition
Bayesian learning mechanisms update beliefs about model parameters using Bayes’ theorem, combining prior knowledge with observed data to form a posterior distribution. Unlike frequentist approaches that seek point estimates, these methods provide a full distribution over possible parameter values, enabling natural regularization and uncertainty quantification. Common techniques include Variational Inference and Markov Chain Monte Carlo sampling, which approximate the posterior when exact computation is intractable.
Summary
Learning paradigms that treat model parameters as random variables with probability distributions rather than fixed values.
Key Concepts
- Posterior Distribution
- Prior Belief
- Uncertainty Quantification
- Bayes’ Theorem
Use Cases
- Small dataset learning with strong priors
- Risk-sensitive decision making
- Active learning strategies