1. An easy way to think about priors on linear regression
  2. Combining priors and downweighting in linear regression
  3. Metropolis Hastings MCMC when the proposal and target have differing support
  4. Slidify: Things are coming together fast
  5. How to Convert Sweave LaTeX to knitr R Markdown: Winter Olympic Medals Example
  6. Testing R Markdown with R Studio and posting it on RPubs.com
  7. Announcing The R markdown Package
  8. Announcing RPubs: A New Web Publishing Service for R
  9. Approximate Bayesian computation
  10. Load Packages Automatically in RStudio
  11. Practical advice for machine learning: bias, variance and what to do next
  12. The overview article on “Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis” associated with the invited talk at the upcoming PODS 2012 meeting is on the arXiv here.
  13. The monograph on “Randomized Algorithms for Matrices and Data” is available in NOW’s “Foundations and Trends in Machine Learning” series here, and it is also available on the arXiv here.
  14. Click here for information (including the slides and video!) on the Tutorial on “Geometric Tools for Identifying Structure in Large Social and Information Networks,” given originally at ICML10 and KDD10 and subsequently at many other places. (The slides are also linked to below.)
  15. The overview chapter on “Algorithmic and Statistical Perspectives on Large-Scale Data Analysis” is finally on the arXiv here; the book in which it will appear is in press; and a video of the associated talk is here.
  16. Recent teaching: Fall 2009: CS369M: Algorithms for Massive Data Set Analysis
  17. Confidence distributions
  18. Making a singular matrix non-singular
  19. Statistics Versus Machine Learning
  20. How to post R code on WordPress blogs
  22. Pro Tips for Grad Students in Statistics/Biostatistics (Part 1)
  23. Pro Tips for Grad Students in Statistics/Biostatistics (Part 2)
  24. Why You Shouldn’t Conclude “No Effect” from Statistically Insignificant Slopes
  25. For those interested in knitr with Rmarkdown to beamer slides
  26. Notes from A Recent Spatial R Class I Gave
  27. Sparse Bayesian Methods for Low-Rank Matrix Estimation and Bayesian Group-Sparse Modeling and Variational Inference – implementation
  28. The Battle of the Bayes
  29. Ockham Workshop, Day 1
  30. Ockham Workshop, Day 2
  31. Ockham Workshop, Day 3
  32. Ockham’s Razor
  33. Occam