User:Timothee Flutre/Notebook/Postdoc/2011/11/16

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About statistical modeling

  • great courses:
    • "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free online book)
    • "A First Course in Bayesian Statistical Methods" by Peter Hoff (book)
    • "Bayesian Data Analysis" by Andrew Gelman (free online slides, book)
  • mathematical aspects:
    • "Introduction to Linear Algebra" by Gilbert Strang (free online videos, book)
    • "Matrix Differential Calculus with Applications in Statistics and Econometrics" by Magnus and Neudecker (free online pdf, book)
  • practical, computational aspects:
    • "Exploratory Data Analysis with R" by Jennifer Bryan (free online course)
    • "Tutorial on Big Data with Python" by Marcel Caraciolo (free online Python notebooks)
    • interpreted languages: obviously R, but more and more Python (SciPy for NumPy, IPython, Matplotlib, and pandas, but also scikit-learn and statsmodels), as well as others (Julia?)
    • C/C++: GSL, Armadillo, Eigen, Rcpp, Stan
    • editor: Emacs
  • visualizing, plotting:
    • "Visualizing uncertainty about the future" by Spiegelhalter et al. (Science 2011, DOI)
    • "Let's practice what we preach: turning tables into graphs" by Gelman et al. (The American Statistician 2002, DOI)
  • philosophy, history:
    • "Mathematical Models and Reality: A Constructivist Perspective" by Christian Hennig (Foundations of Science 2007, DOI)
    • "Philosophy and the practice of Bayesian statistics" by Andrew Gelman and Cosma Shalizi (British Journal of Mathematical and Statistical Psychology 2013, DOI)
    • "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" by Christian Robert (chapitre 2013, free online pdf)
  • classics:
    • list from Christian Robert