Statistics 220 - Articles

Stat 221 Statistical Computing Lecture Notes

The following are the lecture notes from Statistics 221 taught Spring 2004.

Bayes History

  • Bayes T (1763). An Essay Towards Solving a Problem in the Doctrine of Chances. Philosophical Transactions 53: 370-418. PDF file. PDF file of reprint in Biometrika 45: 296-315.

  • Bellhouse DR (2004). The Reverend Thomas Bayes, FRS: A Biography to Celebrate the Tercenternary of His Birth. Statistical Science 19: 3-43. PDF file.

  • Stigler SM (1983). Who Discovered Bayes's Theorem? American Statistician 37: 290-296. PDF file.

Examples of Bayesian Analyses

  • Berliner LM, Wikle CK , and Cressie N (2000). Long-Lead Prediction of Pacific SSTs via Bayesian Dynamic Modeling. Journal of Climate, 13: 3953-3968. PDF file.

  • Berliner LM (2000). Hierarchical Bayesian Modeling in the Environmental Sciences. Allgemeines Statistisches Archiv, Journal of the German Statistical Society 84: 141-153.

  • Clark JS (2005). Why Environmental Scientists are Becoming Bayesians. Ecology Letters 8: 2-14. PDF file.

  • McMillan N, Bortnick SM, Irwin ME, and Berliner LM (2005). A Hierarchical Bayesian Model to Estimate and Forecast Ozone Through Space and Time. Atmospheric Environment 39: 1373-1382. PDF file

  • Shermer M (2004). God's Number Is Up. Scientific American, July 2004: 46. PDF file. Note that there is a typo in Shermer's calculations. The last sentence of the second to last paragraph should have read
    I estimate the probability that God exists is 0.0002, or 0.02 percent.

Monte Carlo

  • Casella G and George EI (1992). Explaining the Gibbs Sampler. American Statistician 46: 167-174. PDF file.

  • Chib S and Greenberg E (1995). Understanding the Metropolis-Hastings Algorithm. American Statistician 49:327-335. PDF file.

  • Flury BD (1990). Acceptance-rejection Sampling Made Easy. Siam Review 32: 474-476. PDF file.

  • Hastings WK (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika 1: 97-109. PDF file.

  • Irwin M, Cox N, and Kong A (1994). Sequential Imputation for Multilocus Linkage Analysis. Proceedings of the National Academy of Sciences of the USA 91: 11684-11688. PDF file.

  • Irwin ME, Cressie N, and Johannesson G (2002). Spatial-temporal nonlinear filtering based on hierarchical statistical models (with discussion). Test 11:249-302. PDF file.

  • Kong A, Liu JS, and Wong WH (1994). Sequential Imputations and Bayesian Missing Data Problems. JASA 89: 278-288. PDF file.

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, and Teller E (1953). Equations of State Calculations by Fast Computings Machines. Journal of Chemical Physics 21: 1087-1092.

  • van der Merwe R, Doucet A, de Freitas N, and Wan E (2000). The Unscented Particle Filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department. PDF file.

EM Algorithm and Extentions

  • Dempster AP, Laird NM, and Rubin DB (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B 38: 1-38. PDF file.

  • Louis TA (1982). Finding the Observed Information Matrix when Using the EM Algorithm. Journal of the Royal Statistical Society. Series B 44: 226-233. PDF file.

  • Meng XL and Rubin DB (1991). Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm. JASA 86: 899-909. PDF file.

  • Meng XL and Rubin DB (1993). Maximum Likelihood Estimation via the ECM Algorithm: A General Framework. Biometrika 80: 267-278. PDF file.

  • Wu CFJ (1983). On the Convergence Properties of the EM Algorithm. The Annals of Statistics 11:95-103. PDF file.

Harvard Statistics
Department of Statistics
Harvard University
Harvard University