Conditional probability is a central idea, where we compute the probability of an event "A" occurring given that we also have information about an event "B" occurring. For example, if I roll a fair dice, event "A" might be that I roll a 6 and event "B" might be that I roll higher than a 3. If someone tells me that "B" definitely occurred, then it changes the probability of "A", now that I know that "B" is true. This will be a fundamental concept when we develop Bayesian statistics.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
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00:00 Intro
01:56 Defining P(A|B)
04:52 Example: Dice
06:37 Example: Cards
08:42 Example: Cancer Screening
11:19 Inference & Outro