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Decision theory and Bayesian statistics

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This chapter outlines some of our more effective demonstrations for teaching decision theory and Bayesian statistics. Our contribution here is in the tricks used to involve students; the ideas behind most of the demonstrations are well known. The activities serve several purposes, including focusing student attention on difficult conceptual issues that are hard to learn in a lecture or by solving homework problems (e.g., the principle of expected gain and determining the value of a life); alerting students to their cognitive illusions (e.g., the incoherent utilities for money and uncalibrated subjective probability intervals); bringing personal issues into the class (e.g., different areas of knowledge in the subjective probability intervals and personal decision problems); dramatizing counterintuitive results which a student might not realize as counterintuitive; and demonstrating the multiple levels of uncertainty in a Bayesian analysis, as well as the coverage property of posterior intervals.
Title: Decision theory and Bayesian statistics
Description:
This chapter outlines some of our more effective demonstrations for teaching decision theory and Bayesian statistics.
Our contribution here is in the tricks used to involve students; the ideas behind most of the demonstrations are well known.
The activities serve several purposes, including focusing student attention on difficult conceptual issues that are hard to learn in a lecture or by solving homework problems (e.
g.
, the principle of expected gain and determining the value of a life); alerting students to their cognitive illusions (e.
g.
, the incoherent utilities for money and uncalibrated subjective probability intervals); bringing personal issues into the class (e.
g.
, different areas of knowledge in the subjective probability intervals and personal decision problems); dramatizing counterintuitive results which a student might not realize as counterintuitive; and demonstrating the multiple levels of uncertainty in a Bayesian analysis, as well as the coverage property of posterior intervals.

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