Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2025-01-03T22:19:35.503Z Has data issue: false hasContentIssue false
  • English
  • Français

Teaching Bayesian Statistics

Published online by Cambridge University Press:  21 December 2021

Diogo Ferrari Open the ORCID record for Diogo Ferrari [Opens in a new window]
Diogo Ferrari*
Affiliation:
University of California, Riverside, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Teaching Political Methodology
Information
PS: Political Science & Politics , Volume 55 , Issue 1 , January 2022 , pp. 230 - 235
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Albert, Jim. 1997. “Teaching Bayes’ Rule: A Data-Oriented Approach.” The American Statistician 51 (3): 247–53.Google Scholar
Berry, Donald A. 1997. “Teaching Elementary Bayesian Statistics with Real Applications in Science.” The American Statistician 51 (3): 241–46.Google Scholar
Christensen, Ronald. 2005. “Testing Fisher, Neyman, Pearson, and Bayes.” The American Statistician 59 (2): 121–26.10.1198/000313005X20871CrossRefGoogle Scholar
Gelfand, Alan E., and Smith, Adrian F.. 1990. “Sampling-Based Approaches to Calculating Marginal Densities.” Journal of the American Statistical Association 85 (410): 398–409.10.1080/01621459.1990.10476213CrossRefGoogle Scholar
Gelman, Andrew. 2005. “A Course on Teaching Statistics at the University Level.” The American Statistician 59 (1): 4–7.10.1198/000313005X24110CrossRefGoogle Scholar
Gelman, Andrew, Carlin, John B., Stern, Hal S., and Rubin, Donald B.. 2014. Bayesian Data Analysis. Second edition. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Gelman, Andrew and Nolan, Deborah A.. 2017. Teaching Statistics: A Bag of Tricks. Oxford: Oxford University Press.10.1093/oso/9780198785699.001.0001CrossRefGoogle Scholar
Geman, Stuart, and Geman, Donald. 1984. “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 6:721–41.10.1109/TPAMI.1984.4767596CrossRefGoogle ScholarPubMed
Ghosh, Jayanta K., Delampady, Mohan, and Samanta, Tapas. 2007. An Introduction to Bayesian Analysis: Theory and Methods. Berlin, Germany: Springer Science & Business Media.Google Scholar
Gill, Jeff. 2014. Bayesian Methods: A Social and Behavioral Sciences Approach. Boca Raton, FL: CRC Press.10.1201/b17888CrossRefGoogle Scholar
Hastings, Wilfred K. 1970. “Monte Carlo Sampling Methods Using Markov Chains and Their Applications.” Biometrika 57 (1): 97–109.10.1093/biomet/57.1.97CrossRefGoogle Scholar
Hoff, Peter D. 2009. A First Course in Bayesian Statistical Methods. New York: Springer.10.1007/978-0-387-92407-6CrossRefGoogle Scholar
Jackman, Simon. 2004. “Bayesian Analysis for Political Research.” Annual Review of Political Science 7:483–505.10.1146/annurev.polisci.7.012003.104706CrossRefGoogle Scholar
Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Hoboken, NJ: John Wiley & Sons.10.1002/9780470686621CrossRefGoogle Scholar
Kruschke, John. 2015. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Second edition. Cambridge, MA: Academic Press.Google Scholar
Metropolis, Nicholas, Rosenbluth, Arianna W., Rosenbluth, Marshall N., Teller, Augusta H., and Teller, Edward. 1953. “Equation of State Calculations by Fast Computing Machines.” Journal of Chemical Physics 21 (6): 1087–92.10.1063/1.1699114CrossRefGoogle Scholar
Moore, David S. 1997. “Bayes for Beginners? Some Pedagogical Questions.” In Advances in Statistical Decision Theory and Applications, ed. Panchapakesan, Subramanian and Balakrishnan, Narayanaswamy, 3–17. New York: Springer.10.1007/978-1-4612-2308-5_1CrossRefGoogle Scholar
Utts, Jessica, and Johnson, Wesley O.. 2008. “The Evolution of Teaching Bayesian Statistics to Nonstatisticians: A Partisan View from the Trenches.” The American Statistician 62 (3): 199–201.10.1198/000313008X330810CrossRefGoogle Scholar