bayesm

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 71 articles )

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  1. Diani, Cecilia; Galimberti, Giuliano; Soffritti, Gabriele: Multivariate cluster-weighted models based on seemingly unrelated linear regression (2022)
  2. Hein, Maren; Goeken, Nils; Kurz, Peter; Steiner, Winfried J.: Using hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: a study based on conjoint choice data (2022)
  3. Ru, Zice; Liu, Jiapeng; Kadziński, Miłosz; Liao, Xiuwu: Bayesian ordinal regression for multiple criteria choice and ranking (2022)
  4. Sinha Roy, Debdatta; Defryn, Christof; Golden, Bruce; Wasil, Edward: Data-driven optimization and statistical modeling to improve meter reading for utility companies (2022)
  5. Tulabandhula, Theja; Sinha, Deeksha; Karra, Saketh: Optimizing revenue while showing relevant assortments at scale (2022)
  6. Bansal, Prateek; Krueger, Rico; Graham, Daniel J.: Fast Bayesian estimation of spatial count data models (2021)
  7. Galimberti, Giuliano; Nuzzi, Lorenzo; Soffritti, Gabriele: Covariance matrix estimation of the maximum likelihood estimator in multivariate clusterwise linear regression (2021)
  8. Obiang, Eunice Okome; Jézéquel, Pascal; Proïa, Frédéric: A partial graphical model with a structural prior on the direct links between predictors and responses (2021)
  9. Osmundsen, Kjartan Kloster; Kleppe, Tore Selland; Oglend, Atle: MCMC for Markov-switching models -- Gibbs sampling vs. marginalized likelihood (2021)
  10. Weber, Anett; Steiner, Winfried J.: Modeling price response from retail sales: an empirical comparison of models with different representations of heterogeneity (2021)
  11. Anderson, Gordon; Pittau, Maria Grazia; Zelli, Roberto: Measuring the progress of equality of educational opportunity in absence of cardinal comparability (2020)
  12. Dalla Valle, Luciana; Leisen, Fabrizio; Rossini, Luca; Zhu, Weixuan: Bayesian analysis of immigration in Europe with generalized logistic regression (2020)
  13. Frits Traets, Daniel Gil Sanchez, Martina Vandebroek: Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package (2020) not zbMATH
  14. Galimberti, Giuliano; Soffritti, Gabriele: Seemingly unrelated clusterwise linear regression (2020)
  15. Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
  16. Reichl, Johannes: Estimating marginal likelihoods from the posterior draws through a geometric identity (2020)
  17. Smith, Adam N.; Allenby, Greg M.: Demand models with random partitions (2020)
  18. Yves Croissant: Estimation of Random Utility Models in R: The mlogit Package (2020) not zbMATH
  19. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  20. Magnus, Gideon; Magnus, Jan R.: The estimation of normal mixtures with latent variables (2019)

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