Mathematics: Applications and Interpretation HL

Probability distributions and decision models

Discrete and continuous probability models, Bayes reasoning, expectation, variance, and risk decisions for AI HL.

Use discrete probability distributions Calculate probability, expectation, or variance from a discrete distribution. Model counts with the Poisson distribution Use Poisson probabilities and expected rates in applied count models. Use normal approximations to binomial models Apply mean, standard deviation, and continuity correction for binomial approximations. Use Bayes theorem in decision contexts Use prior probabilities and likelihoods to calculate posterior probabilities. Use expectation and variance rules Use linearity of expectation and variance rules for transformed or combined variables. Use continuous random variables Use density functions and area reasoning for continuous probability models.