Statistics and Probability

• Generalized Linear Models by Germán Rodríguez
• StatLect by Marco Taboga
• STAT 414 Introduction to Probability Theory from the Eberly College of Science
• STAT 415 Introduction to Mathematical Statistics from the Eberly College of Science
• Random: Probability, Mathematical Statistics, Stochastic Processes Kyle Siegrist (Uni. of Alabama)
• Introduction to Probability, Statistics and Random Processes by Hossein Pishro-Nik (2014; Kappa Research LLC)
• Probability: Theory and Examples by Rick Durrett (2019)
• Statistics 200: Introduction to Statistical Inference by Zhou Fan (Stanford University, Autumn 2016)
  • Lecture 13 — Maximum likelihood estimation
  • Lecture 14 — Consistency and asymptotic normality of the MLE
  • Lecture 15 — Fisher information and the Cramer-Rao bound
• Stats 202 · Stanford University
• Inference! An interactive introduction
• Notes on Probability* by Greg Lawler [PDF]
• Probability: Theory and Examples by Rick Durrett (2019) (also via Drive)
• STAT 400 by John Millson University of Maryland. See Handouts for Stat 400 and Stat 401
• Regression Modeling Strategies by Frank Harrell
• Probabilstic Graphical Models Notes (CS228)
• Advanced Statistical Computing
• Improving Your Statistical Inferences

Bayesian Statistics (inc. Stochastic Processes)

• Scribe Notes from Bayesian Modeling and Inference (Stat260) by Michael I. Jordan
• A First Course in Bayesian Statistical Methods by Peter D. Hoff
• Monte Carlo Methods (Arizona Math 577-002 2016) by Tom Kennedy
• Computational Cognition Cheat Sheets from various authors at Robert Jacobs’ Computational Cognition and Perception Lab
• Bayesian Modeling and Computation in Python by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao
• Applied Stochastic Analysis by Miranda Holmes-Cerfon