Probability Trees and Conditional ExpectationsLocked: included in All Access

Probability tools for investment decisions under uncertainty: expected value and dispersion of a random outcome, probability trees with conditional expectations, and Bayes' formula for updating a probability when new information arrives.

1. 1. Expected value, variance and standard deviationExpected value is your probability-weighted forecast; variance and standard deviation measure how far the actual outcome is likely to land from that forecast.Video · 7 minLocked: included in All Access14 min
2. 2. Conditional probability and probability treesA probability tree splits an uncertain outcome into scenarios and then into outcomes conditional on each scenario, so joint and total probabilities can be read straight off its branches.Locked: included in All Access14 min
3. 3. Conditional expected values and variancesWork out the expected value inside each scenario, then weight those conditional expectations by the scenario probabilities: the result must match the overall forecast.Video · 6 minLocked: included in All Access15 min
4. 4. Bayes' formula: updating a probability with new informationBayes' formula turns a prior probability into a posterior probability by multiplying it by how much more, or less, likely the new information is when the event is true.Video · 5 minLocked: included in All Access15 min
5. 5. Bayes in practice: diffuse priors, missing inputs and screening testsExam Bayes problems usually hide one input: you may need to back it out with the total probability rule, use complements, or start from equal (diffuse) priors.Locked: included in All Access14 min