PSYC 315 Lecture Notes - Lecture 11: Causal Reasoning, Prior Probability, Foodborne Illness
16 Jan 2018
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Rational updating of belief given new evidence. Collecting hypothesis and show subject the hypothesis. Use bayes rule on how to update hypothesis. Exist model between symbolic structure and sub-symbolic neural networks. Next level is algorithmic (symbolic rule processing) Next level is implementation (brain-like structures or what?) Bayes can be compatible but at a higher mathematical level. Start with two random variables a and b. Taking a particular value a of a: p(a, b) = p(a|b) p(b, p(a, b) = p(b|a) p(a, p(a|b) p(b) = p(b|a) p(a, p(a|b) = p(b|a) p(a) / p(b, p(h|d) = p(d|h) p(h) / p(d) P(h|d) : posterior - what should we believe about hypothesis given the data. P(d|h) : likelihood hypothesis of data occurring given hypothesis was true. P(h) : prior - probability of hypothesis being true without any data. P(d) : marginal probability of the data: p(d) = p(h,d) The posterior from last trial is the next prior.
