Overview
Bayesian inference differs from classical (frequentist) statistics by treating probability as a 'degree of belief.' It allows for the incorporation of prior knowledge into the analysis.
Key Components
- Prior: Initial belief about a parameter before seeing data.
- Likelihood: The probability of the observed data given the parameter.
- Posterior: The updated belief about the parameter after seeing the data.
Formula
Posterior ∝ Likelihood × Prior
Use Cases
- Spam filtering.
- Medical diagnosis.
- Real-time tracking and navigation.