Bayesian (inverse probability) inference in games : Part two

The second of two sceencast lectures on how intelligent players reason about states and signals in a game ; the first is here
This lecture uses the two practise problems from class (one on breast cancer screening, the other on witness reports in a courtroom case) to develop a “language” of probability in a way  that is (1) easily understandable for ANY type or level of student – whether trained in statistics or not and (2) useful for students of game theory . Simple numerical examples using Gigerenzer style natural frequency/count reasoning are developed to explain a wide range of concepts connecting uncertainties about “states” and “signals” sensitivity, specificity, conditional probabilities, predictive probabilities, inverse probabilities, etc.