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5: Conditional Independence

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    • 5.1: Conditional Independence
      This page covers conditional independence in probability theory, defining it as the independence of events A and B given another event C. It explains the underlying probabilistic principles and includes examples related to consumer behavior and academic performance. The page also introduces techniques for evaluating claims through a race track scenario, illustrating how to assess probabilities and validate assertions using odds ratios.
    • 5.2: Patterns of Probable Inference
      This page covers the assessment and calculation of probabilities related to hypotheses using Bayes' rule, focusing on how evidence updates prior odds through various examples, such as medical tests and market analysis. It discusses conditional probabilities, classification procedures for different groups based on survey data, and provides computational methods using MATLAB and specific m-files for efficient classification.
    • 5.3: Problems on Conditional Independence
      This page covers various exercises on probability, emphasizing conditional probabilities and Bayesian reasoning. Key topics include the independence of events, posterior odds calculation, and applying Bayes' theorem to real-world scenarios such as sports records, real estate, medical diagnostics, geological surveys, and product launch implications.

    This page titled 5: Conditional Independence was last modified on Sun, 25 Oct 2026 20:34:12 GMT and is shared under a CC BY 3.0 license and was authored, remixed, and/or curated by Paul Pfeiffer via source content that was edited to the style and standards of the LibreTexts platform.