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Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
Trafimow, David – Teaching Statistics: An International Journal for Teachers, 2011
Students often have difficulty understanding algebraic proofs of statistics theorems. However, it sometimes is possible to prove statistical theorems with pictures in which case students can gain understanding more easily. I provide examples for two versions of Bayes' theorem.
Descriptors: Visual Aids, Bayesian Statistics, Mathematical Logic, Validity
CadwalladerOlsker, Todd D. – Mathematics Teacher, 2011
Bayes's theorem is notorious for being a difficult topic to learn and to teach. Problems involving Bayes's theorem (either implicitly or explicitly) generally involve calculations based on two or more given probabilities and their complements. Further, a correct solution depends on students' ability to interpret the problem correctly. Most people…
Descriptors: Critical Thinking, Probability, Mathematical Logic, Mathematics Skills

Sahai, Hardeo; Reesal, Michael R. – School Science and Mathematics, 1992
Illustrates some applications of elementary probability and statistics to epidemiology, the branch of medical science that attempts to discover associations between events, patterns, and the cause of disease in human populations. Uses real-life examples involving cancer's link to smoking and the AIDS virus. (MDH)
Descriptors: Bayesian Statistics, Epidemiology, Integrated Activities, Mathematical Applications