Bayesian Inference Explained
Bayesian inference offers a powerful perspective on probability, treating unknown quantities as distributions that update when new evidence appears.
Priors and Posteriors
The process begins with a prior distribution that captures our initial beliefs about a parameter. After observing data, we apply Bayes’ theorem to obtain the posterior distribution, reflecting how our beliefs should change.
Why Use Bayesian Methods?
Bayesian techniques are particularly useful when data is scarce or when incorporating domain knowledge is essential. They provide a coherent approach to uncertainty that can complement or outperform classical methods in many situations.
