3: Conditional Probability
- Page ID
- 10814
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The probability P(A) of an event A is a measure of the likelihood that the event will occur on any trial. Sometimes partial information determines that an event C has occurred. Given this information, it may be necessary to reassign the likelihood for each event A. This leads to the notion of conditional probability. For a fixed conditioning event C, this assignment to all events constitutes a new probability measure which has all the properties of the original probability measure. In addition, because of the way it is derived from the original, the conditional probability measure has a number of special properties which are important in applications.
- 3.1: Conditional Probability
- This page covers the fundamentals of probability, focusing on prior and conditional probability measures, and the importance of updating probabilities with new information. It explains the mathematical foundation of conditional probability, including Bayes' theorem and the law of total probability, with practical examples in hiring decisions, medical tests, and safety assessments.
- 3.2: Problems on Conditional Probability
- This page presents exercises on conditional probability and related statistical concepts, covering a range of scenarios from demographics to product reliability. It explores practical applications of Bayes' theorem and the implications of biases in reporting, using examples like survey data and income levels.


