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 48 articles )

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  1. Smith, Adam N.; Allenby, Greg M.: Demand models with random partitions (2020)
  2. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  3. Kim, Sunghoon; DeSarbo, Wayne S.; Fong, Duncan K. H.: A hierarchical Bayesian approach for examining heterogeneity in choice decisions (2018)
  4. Kleyn, J.; Arashi, M.; Millard, S.: Preliminary test estimation in system regression models in view of asymmetry (2018)
  5. Qiu, Jinwen; Jammalamadaka, S. Rao; Ning, Ning: Multivariate Bayesian structural time series model (2018)
  6. Bertsimas, Dimitris; Mišić, Velibor V.: Robust product line design (2017)
  7. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017) not zbMATH
  8. Depraetere, Nicolas; Vandebroek, Martina: A comparison of variational approximations for fast inference in mixed logit models (2017)
  9. Luo, Ronghua; Lan, Wei: Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm (2017)
  10. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017) not zbMATH
  11. Paetz, Friederike; Steiner, Winfried J.: The benefits of incorporating utility dependencies in finite mixture probit models (2017)
  12. Tan, Linda S. L.: Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes (2017)
  13. Weber, Anett; Steiner, Winfried J.; Lang, Stefan: A comparison of semiparametric and heterogeneous store sales models for optimal category pricing (2017)
  14. Zhang, Wei; Mandal, Abhyuday; Stufken, John: Approximations of the information matrix for a panel mixed logit model (2017)
  15. Fong, Duncan K. H.; Kim, Sunghoon; Chen, Zhe; DeSarbo, Wayne S.: A Bayesian multinomial probit model for the analysis of panel choice data (2016)
  16. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  17. Ma, Shaohui; Hou, Lu; Yao, Wensong; Lee, Baozhen: A nonhomogeneous hidden Markov model of response dynamics and mailing optimization in direct marketing (2016)
  18. Benavoli, Alessio; Mangili, Francesca; Ruggeri, Fabrizio; Zaffalon, Marco: Imprecise Dirichlet process with application to the hypothesis test on the probability that (X \leqY) (2015)
  19. Cederburg, Scott; O’Doherty, Michael S.: Asset-pricing anomalies at the firm level (2015)
  20. Mahani, Alireza S.; Sharabiani, Mansour T. A.: SIMD parallel MCMC sampling with applications for big-data Bayesian analytics (2015)

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