Teaching Bayesian statistics to marketing and business students. We discuss our experiences teaching Bayesian statistics to students in doctoral programs in business. These students often have weak backgrounds in mathematical statistics and a predisposition against likelihood-based methods stemming from prior exposure to econometrics. This can be overcome by an intense course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success of our course is primarily due to the emphasis on statistical computing. This is facilitated by our R package, bayesm, which provides efficient implementation of advanced methods and models.

References in zbMATH (referenced in 56 articles )

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  1. Bansal, Prateek; Krueger, Rico; Graham, Daniel J.: Fast Bayesian estimation of spatial count data models (2021)
  2. Anderson, Gordon; Pittau, Maria Grazia; Zelli, Roberto: Measuring the progress of equality of educational opportunity in absence of cardinal comparability (2020)
  3. Frits Traets, Daniel Gil Sanchez, Martina Vandebroek: Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package (2020) not zbMATH
  4. Galimberti, Giuliano; Soffritti, Gabriele: Seemingly unrelated clusterwise linear regression (2020)
  5. Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
  6. Reichl, Johannes: Estimating marginal likelihoods from the posterior draws through a geometric identity (2020)
  7. Smith, Adam N.; Allenby, Greg M.: Demand models with random partitions (2020)
  8. Yves Croissant: Estimation of Random Utility Models in R: The mlogit Package (2020) not zbMATH
  9. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  10. Jagabathula, Srikanth; Subramanian, Lakshminarayanan; Venkataraman, Ashwin: A model-based embedding technique for segmenting customers (2018)
  11. Kim, Sunghoon; DeSarbo, Wayne S.; Fong, Duncan K. H.: A hierarchical Bayesian approach for examining heterogeneity in choice decisions (2018)
  12. Kleyn, J.; Arashi, M.; Millard, S.: Preliminary test estimation in system regression models in view of asymmetry (2018)
  13. Qiu, Jinwen; Jammalamadaka, S. Rao; Ning, Ning: Multivariate Bayesian structural time series model (2018)
  14. Bertsimas, Dimitris; Mišić, Velibor V.: Robust product line design (2017)
  15. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017) not zbMATH
  16. Depraetere, Nicolas; Vandebroek, Martina: A comparison of variational approximations for fast inference in mixed logit models (2017)
  17. Luo, Ronghua; Lan, Wei: Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm (2017)
  18. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017) not zbMATH
  19. Paetz, Friederike; Steiner, Winfried J.: The benefits of incorporating utility dependencies in finite mixture probit models (2017)
  20. Tan, Linda S. L.: Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes (2017)

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