Definition
A ‘prior’ represents existing beliefs or historical data regarding a variable before incorporating new observations. In Bayesian inference, the prior is combined with the likelihood of the observed data to compute the posterior distribution. This concept is crucial in machine learning for regularization, where priors encode assumptions about model complexity or sparsity. Choosing an appropriate prior can significantly influence model behavior, especially when data is scarce.
Summary
In Bayesian statistics, a probability distribution expressing knowledge or belief about a parameter before observing new evidence or data.
Key Concepts
- Bayesian Inference
- Probability Distribution
- Regularization
- Posterior
Use Cases
- Implementing Bayesian Neural Networks
- Applying L1/L2 regularization as priors
- Updating medical diagnoses with new test results