Here is a link to the shared deck. Major sources:

  • Judea Pearl’s “Causal inference in statistics: An overview”; paperpdf. Pearl calls this “everything I know about statistics in only 40 pages.” Under the “PERL” tag.
  • Edwin Jaynes’ Probability Theory: The Logic of Science; amazon, pdf of TOC and preface. Heavily Bayesian. Under the “JAYN” tag. 
  • Bayesian Data Analysis, by Devinderjit Sivia and John Skilling; amazon. The first five chapters. Noted as the “S&S” reference.
  • Wikipedia’s list of probability distributions, the major ones. These articles are a major source for the “DSTN” tag.
  • Lindsay Smith’s Tutorial on PCA, pdf. Philipp Janert’s book Data Analysis with Open Source Tools, google books, the chapter on PCA. Wikipedia’s page on eigenvalues and eigenvectors. All under the “EIGN” tag, because PCA is just finding the eigenvalues of the covariance matrix.
  • Robert Andersen’s “Nonparametric Methods for Modeling Nonlinearity in Regression Analysis”, article. Under the “NLRG” (non-linear regression) tag.