1,712
Views
5
CrossRef citations to date
0
Altmetric
Bayesian Cluster

Bayesian Computing in the Undergraduate Statistics Curriculum

&

References

  • Albert, J. (2000), “Using a Sample Survey Project to Compare Classical and Bayesian Approaches for Teaching Statistical Inference,” Journal of Statistics Education, 8, 1–10.
  • Albert, J. (2018), “LearnBayes: Functions for Learning Bayesian Inference,” R Package Version 2.15.1, available at https://CRAN.R-project.org/package=LearnBayes.
  • Albert, J., and Hu, J. (2019), Probability and Bayesian Modeling, Texts in Statistical Science, Boca Raton, FL: CRC Press.
  • Berry, D. A. (1996), Statistics: A Bayesian Perspective, Belmont, CA: Duxbury Press.
  • Berry, D. A. (1997), “Teaching Elementary Bayesian Statistics With Real Applications in Science,” The American Statistician, 51, 241–246.
  • Betancout, M. (2017), “A Conceptual Introduction to Hamiltonian Monte Carlo,” arXiv no. 1701.02434.
  • Blackwell, D. (1969), Basic Statistics, New York: McGraw Hill.
  • Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017), “Stan: A Probabilistic Programming Language,” Journal of Statistical Software, 76, 1–32. DOI: 10.18637/jss.v076.i01.
  • Casella, G., and George, E. I. (1992), “Explaining the Gibbs Sampler,” The American Statistician, 46, 167–174.
  • Chib, S., and Greenberg, E. (1995), “Understanding the Metropolis-Hastings algorithm,” The American Statistician, 49, 327–335.
  • Cowles, M. K., and Carlin, B. P. (1996), “Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review,” Journal of the American Statistical Association, 91, 883–904. DOI: 10.1080/01621459.1996.10476956.
  • Denwood, M. J. (2016), “runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS,” Journal of Statistical Software, 71, 1–25. DOI: 10.18637/jss.v071.i09.
  • Gelfand, A. E., Hills, S. E., Racine-Poon, A., and Smith, A. F. (1990), “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling,” Journal of the American Statistical Association, 85, 972– 985. DOI: 10.1080/01621459.1990.10474968.
  • Gelfand, A. E., and Smith, A. F. M. (1990), “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of the American Statistical Association, 85, 398–409. DOI: 10.1080/01621459.1990.10476213.
  • Gelman, A., Roberts, G. O., and Gilks, W. R. (1996), “Efficient Metropolis Jumping Rules,” Bayesian Statistics, 5, 599–607.
  • Kruschke, J. K. (2015), Doing Bayesian Analysis: A Tutorial With R, JAGS, and Stan, Burlington, MA: Academic Press.
  • Lee, P. M. (1997), Bayesian Statistics, London: Arnold Publication.
  • Lunn, D., Spiegelhalter, D., Thomas, A., and Best, N. (2009), “The BUGS Project: Evolution, Critique and Future Directions,” Statistics in Medicine, 28, 3049–3067. DOI: 10.1002/sim.3680.
  • Martz, H. F., and Waller, R. (1982), Bayesian Reliability Analysis, New York: Wiley.
  • McElreath, R. (2020), Statistical Rethinking: A Bayesian Course With Examples in R and Stan, Texts in Statistical Science (2nd ed.), Boca Raton, FL: CRC Press.
  • Mengersen, K. L., Robert, C. P., and Guihenneuc-Jouyaux, C. (1999), “MCMC Convergence Diagnostics: A Review,” Bayesian Statistics, 6, 415–440.
  • Neal, R. M. (2011), “MCMC Using Hamiltonian Dynamics,” in Handbook of Markov Chain Monte Carlo, eds. S. Brooks, A. Gelman, G. L. Jones, and X. L. Meng, Boca Raton, FL: Chapman and Hall/CRC, pp. 113–162.
  • Phillips, L. D. (1973), Bayesian Statistics for Social Scientists, London: Thomas Nelson.
  • Plummer, M. (2003), “JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling,” in Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria (Vol. 124), pp. 1–10.
  • Raiffa, H., and Schlaifer, R. (1961), Applied Statistical Decision Theory, Boston, MA: Division of Research, Graduate School of Business Administration, Harvard University.
  • Reich, B. J., and Ghosh, S. K. (2019), Bayesian Statistical Methods, Texts in Statistical Science, Boca Raton, FL: CRC Press.
  • Schmitt, S. (1969), Measuring Uncertainty: An Elementary Introduction to Bayesian Statistics, Reading, MA: Addison-Wesley.
  • Smith, A., Skene, A., Shaw, J., Naylor, J., and Dransfield, M. (1985), “The Implementation of the Bayesian Paradigm,” Communications in Statistics—Theory and Methods, 14, 1079–1102. DOI: 10.1080/03610928508828963.
  • Tierney, L., and Kadane, J. B. (1986), “Accurate Approximations for Posterior Moments and Marginal Densities,” Journal of the American Statistical Association, 81, 82–86. DOI: 10.1080/01621459.1986.10478240.
  • Winkler, R. L. (1972), An Introduction to Bayesian Inference and Decision, New York: Holt, Rinehart and Winston.