Chapter 2: Conditional Probability and Independence

Learning Objectives

• Compute conditional probabilities and apply the multiplication rule and chain rule
• Derive the law of total probability and apply it to partition-based arguments
• State and apply Bayes' theorem in the prior-likelihood-posterior framework
• Distinguish independence, pairwise independence, and mutual independence
• Define conditional independence and state the Markov chain condition
• Derive the binomial, geometric, and negative binomial distributions from repeated Bernoulli trials
• Recognise the memoryless property and identify distributions that possess it