Foundations of Bayesian Thinking

Explain the concept of probability as a 'degree of belief', contrasting it with the frequentist interpretation.

Apply the sum and product rules of probability to calculate conditional probabilities in simple scenarios.

Explain the components of Bayes' theorem (prior, likelihood, posterior, evidence) using a conceptual example like medical diagnosis.

Describe the iterative nature of Bayesian learning, where a posterior from one analysis can become the prior for the next.

Distinguish between estimation, hypothesis testing, and prediction as primary goals of a Bayesian analysis.