Statistics

Courses

• https://github.com/thomas-haslwanter/statsintro_python/tree/master/ipynb
• Model based machine learning: http://www.mbmlbook.com/toc.html
• A free online companion course to the Second Edition of An Introduction to Statistical Learning is available through edX - https://www.statlearning.com/online-course
• Michael Betancourt series on probability, modeling, etc. - https://betanalpha.github.io/writing/

Books

• Wasserman’s All of Statistics
• Shalizi’s Advanced Data Analysis from an Elementary Point of View
• Computer Age Statistical Inference: Algorithms, Evidence and Data Science
• Model Based Machine Learning by John Winn and others (including Chris Bishop)
• Intro to Probability for Data Science
• The Effect: An Introduction to Research Design and Causality
• Bayesian models of perception and action

Probability theory

• https://betanalpha.github.io/assets/case_studies/probability_theory.html
• https://betanalpha.github.io/assets/case_studies/conditional_probability_theory.html

Probabilist programming

• Prob programming workflow using PyMC3 - https://github.com/springcoil/pydataprobprog

Bayesian Data Analysis

• Lecture notes and videos by Aki Vehtari - https://github.com/avehtari/BDA_course_Aalto

Links

• Connection between statistical tests and linear models - https://lindeloev.github.io/tests-as-linear/
• Understanding ANOVA - http://www.stat.columbia.edu/~gelman/research/unpublished/econanova2.pdf
• Intro to probabilistic programming - https://arxiv.org/abs/1809.10756
• Imitation in Animals: Evidence, Function, and Mechanisms - http://pigeon.psy.tufts.edu/avc/zentall/default.htm
• Simpson's paradox - https://roamanalytics.com/2017/09/08/simpsons-paradox-and-causal-inference-with-observational-data/
• A Survey on Data Collection for Machine Learning: a Big Data - AI Integration Perspective - https://arxiv.org/abs/1811.03402
• Data science is science's second chance to get causal inference right: A classification of data science tasks - https://arxiv.org/abs/1804.10846
• High-Confidence Predictions under Adversarial Uncertainty - https://arxiv.org/abs/1101.4446
• Cheat Sheet: Subgradient Descent, Mirror Descent, and Online Learning - http://www.pokutta.com/blog/research/2019/02/27/cheatsheet-nonsmooth.html
• Handy statistical lexicon - https://statmodeling.stat.columbia.edu/2009/05/24/handy_statistic/
• Toward a principled Bayesian workflow: A tutorial for cognitive science - https://osf.io/b2vx9/
• Description of different types of MCMC algorithms - https://m-clark.github.io/docs/ld_mcmc
• Lord's paradox, Simpson's paradox explanations - https://m-clark.github.io/docs/lord/index.html
• Probability cheatsheet - https://github.com/wzchen/probability_cheatsheet
• Importance Samplint - https://statweb.stanford.edu/~owen/mc/Ch-var-is.pdf
• Montel-Carlo - https://github.com/szcf-weiya/MonteCarlo/blob/master/References/Monte-Carlo-Strategies-in-Scientific-Computing.pdf
• The ABC of ABC (Approximate Bayesian Computation) - https://xianblog.wordpress.com/2019/07/28/introductory-overview-lecture-the-abc-of-abc/amp/
• Falling (In Love With Principled Modeling) - https://betanalpha.github.io/assets/case_studies/falling.html
• Handy statistical lexicon from Gelman - https://statmodeling.stat.columbia.edu/2009/05/24/handy_statistic/

Uncertainity

• Monte-Carlo-Strategies-in-Scientific-Computing - https://erikbern.com/2018/10/08/the-hackers-guide-to-uncertainty-estimates.html

Extreme value theory (EVT)

• Novelty detection via EVT: https://pdfs.semanticscholar.org/b81a/a3046e7f9213949fb37e0c59cacdca4572c4.pdf
• Fitting extreme value distribution (Weibull): https://stackoverflow.com/questions/17481672/fitting-a-weibull-distribution-using-scipy
• Extreme value analysis an introduction: https://hal-enac.archives-ouvertes.fr/hal-00917995/document
• EVT in context of discrete choice modeling: https://eml.berkeley.edu/books/choice2.html

Datasets

• https://www.visualdata.io/

Economics

• The Economy - https://www.core-econ.org/the-economy/
• QuantEcon: Open source code for economic modeling - https://quantecon.org/
• Nashpy: Python library used for the computation of equilibria in 2 player strategic form games - https://nashpy.readthedocs.io/en/stable/index.html

Time Series

• Stumpy - https://github.com/TDAmeritrade/stumpy/
• Python Toolkit of Statistics for Pairwise Interactions (pyspi) - https://github.com/olivercliff/pyspi
• Time Series datasets for spatiotemporal data modeling from transportation data imputation project - https://github.com/xinychen/transdim
• Dynamic Mode Decomposition - http://www.pyrunner.com/weblog/2016/07/25/dmd-python/ Application to Traffic Patters