Teaching Bayesian and Markov methods in business analytics curricula: An integrated approach

Abstract

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 Project Life Cycle Management (APLCM) approach and a case study involving qualitative forecasting. The theoretical frameworks for combining Bayesian and Markov methods are developed, and a forecasting solution is implemented in both MS Excel and Python. Based on an assessment of student learning, applying this pedagogical approach helps students better use these disjoint methods and appreciate the value of integrating them. Although this teaching brief is designed and most appropriate for graduate students with previous BA courses, it can also be used in upper-level courses within an undergraduate BA curriculum. Finally, this teaching brief provides the instructors wishing to use this pedagogical approach in their appropriate courses with the necessary resources (i.e., case study, in-class example, and the MS Excel and Python templates).

Building insights on true positives vs. false positives: Bayes’ rule

“…Outside of the traditional formula-based method of teaching Bayes' rule, Bayley et al (2021) tested a gamified way of teaching Bayes' rule and found that it performed similarly to a lecture-based approach. Van der Rhee (2010) describes the use of video clips from a TV show to vividly demonstrate Bayes' rule, while Johnson et al (2022) discuss how instruction on Bayesian analysis and Markov chains can be combined; this is a natural combination since a Markov chain has conditional probabilities in its transition matrix. Glasziou (2001) presents three types of visual displays useful in everyday medical practice to look up updated prior probabilities following test results, including precalculated tables and graphs, programmed Bayes calculators (web-based, Spreadsheet-based, or specialized hand-held calculators), or a nomogram strip of paper (using the odds version of Bayes' rule: posttest odds = pretest odds × likelihood ratio).…”

“…For example, Rouder and Morey (2019) use a ratio form of Bayes' rule since it allows mathematical statistics students to unite parameter estimation and model comparison; their approach for advanced students is beyond the scope of this teaching brief. Similarly, the approach of Johnson et al (2022) requires knowledge of Python programming and Markov chains, both more advanced topics.…”

“…Similarly, the approach of Johnson et al. (2022) requires knowledge of Python programming and Markov chains, both more advanced topics.…”

Able Construction: A Spreadsheet Activity for Teaching Bayes’ Theorem

“…Similarly, a web simulator based on the disappearance of a nuclear submarine shows a practical application of Bayesian probability [12]. Johnson et al [13] integrate Bayesian and Markov methods in their project life cycle management case study, emphasizing the role of these concepts in a marketing and brand loyalty context.…”

Teaching binary logistic regression modeling in an introductory business analytics course

“…Literature on best practices for designing and improving analytics curricula has been growing in recent decades (Cegielski & Jones‐Farmers, 2016; Johnson et al., 2022). However, there is limited literature on teaching binary logistic regression in IBA or statistics courses.…”