Statistics

Bayesian Methods for Hackers - Introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python. (Web)

Text and supporting code for Think Stats, 2nd Edition

Bayes’ Theorem Intuition (2019)

The Little Handbook of Statistical Practice

Introduction to Statistical Learning with Applications in R (Code)

Notes and exercise attempts for "An Introduction to Statistical Learning"

BayesOpt - Toolbox for bayesian optimization, experimental design and stochastic bandits.

Introduction to Empirical Bayes: Examples from Baseball Statistics

Statistical mechanics of deep learning - Surya Ganguli (2019)

Some Useful Probability Facts for Systems Programming (2020)

Graduate Level: Intro to Probability and Statistics

Is it a bad idea to try to predict the stock market with linear regression? (2020)

Statistical Rethinking: A Bayesian Course Using R and Stan (2019 Feb course)

Kalman and Bayesian Filters in Python

Michael I. Jordan statistics courses

Stan - State-of-the-art platform for statistical modeling and high-performance statistical computation. (Code)

Survival analysis in Stan (2022)

Stan Example Models

YAPS - Surface language for programming Stan models using python syntax.

An Overview of Bayesian Inference (2019)

Bayesian Data Analysis (2020) (Web)

Bayesian Data Analysis course material (2020)

Statistics Done Wrong

At the Interface of Algebra and Statistics (2020) (Article)

The Ten Best Ideas in Statistics (2013)

Comprehensive Tutorial on Time Series Modelling and Forecasting (HN)

A Primer on Private Statistics (2020)

pyts - Python package for time series classification.

Causal Data Science with Directed Acyclic Graphs (2019)

Causal Inference in Machine Learning and AI (2020)

Good books on Statistical Methodology and how to approach data problems? (2020)

Probability Theory: The Logic of Science book (Reddit)

Tea language - High-level Language and Runtime System for Automating Statistical Analyses.

Statistical Thinking for the 21st Century - Open source textbook for statistics, with companions for R and Python. (Code)

Learning Statistics with R (Code)

What book do you recommend for how to apply statistics in the real world? (2020)

An Introduction to Monte Carlo (2020)

Forecasting with (un)certainty (2020)

Implementing Naive Bayes in Python (2020)

Linear Regression (2020) (HN)

Logistic Regression from scratch (HN)

Seeing Theory - Visual introduction to probability and statistics. (HN) (HN 2) (Code)

Abusing Linear Regression to Make a Point (2020) (HN)

Introduction to Probability and Random Processes (1979)

Elements of Statistical Learning (HN) (Notes)

Statistics 110: Probability (35 lectures)

Causal Inference Book (2020)

Sets and Probability

Non-Uniform Random Variate Generation (1986)

Bayesian Data Analysis course (2020)

Introduction to the Field of Statistics (and R)

Causal Inference: The Mixtape (2018)

The algebra and machine representation of statistical models (2020)

Introduction to Probability, Statistics, and Random Processes

An Introduction to Conditional Random Fields (2012)

Modeling with Data (2009)

Using python to work with time series data

Structural Time Series book

Introduction to Causal Inference course (2020)

My Master's Degree in Statistics - Curated list of resources.

The Unreasonable Effectiveness of Quasirandom Sequences (2020)

Think in Log Probabilities (2013) (HN)

How to Price an Election: A Martingale Approach (2020)

Bayes’ Theorem

Understanding Statistical Power and Significance Testing (HN)

Statistical Rethinking: A Bayesian Course (with Code Examples in R/Stan/Python/Julia)

Primer to Probability Theory and Its Philosophy (2020)

Interpretation of confidence intervals and Bayesian credible intervals (2020) (HN)

Bayesian Analysis with Python book

Probability Distribution Toolbox - Folklore facts on probability distribution learning, testing, and whatever-ing.

Uncertainty Baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.

Uncertainty Toolbox - Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization. (Tweet)

The medical test paradox: Can redesigning Bayes rule help? (2020)

Probabilistic Machine Learning - Book series by Kevin Murphy.

Predictive Modeling: A Retrospective (2021)

Categories of Nets (2021) (HN)

Statistical Typing: A Runtime Type System for Data Science and Machine Learning (2020)

Empirical methods Course by CMU (Code)

Gaussian Processes: from one to many outputs (2021)

Workshop on Agent-Based Modeling (2021)

Probability, Mathematical Statistics, Stochastic Processes

Stochastic Processes: From Applications to Theory

An Introduction to Hierarchical Modeling (HN)

What are the most important statistical ideas of the past 50 years? (2020) (Article) (HN) (HN)

Comments on ML "versus" statistics (2020)

Nice Statistics term names (2021)

Lisp-Stat - Environment for Statistical Computing. (Code) (HN) (Statistical Analysis with Lisp-Stat)

Causal Inference for The Brave and True - Light-hearted yet rigorous approach to learning impact estimation and sensitivity analysis. (Code)

Statistical approaches for performance analysis (2020)

Bayesian Analysis Recipes

Stanford Nimble - Physics Engine for Deep Learning. (Web)

No, it’s not The Incentives—it’s you (2018)

Probability Theory (For Scientists and Engineers) (2018)

An Introduction to Statistical Learning Book

ISLR tidymodels Labs

Core components of recommender systems (2021)

Interactive Visualization of Gaussian Processes

A Concrete Introduction to Probability (2018) (HN)

Statistical inference considered harmful (2016) (Tweet)

David Hume: Causation

Forecasting: Principles and Practice

Multidimensional Kalman-Filter - Some Python Implementations of the Kalman Filter.

Introduction to Modern Statistics Book - College-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference. (Code)

A Brief Tutorial on Multi-armed Bandits (2021)

Ideas in statistics that have powered AI (2021) (HN)

How to do Bayesian statistical modelling using numpy and PyMC3

Gaussian Processes from Scratch (2019)

Bayesian Modeling and Computation in Python Book (2022) (Code)

Darts, Dice, and Coins: Sampling from a Discrete Distribution (2011)

Probability and Its Applications Book

Mean Field Simulation for Monte Carlo Integration (2013)

Collaborative filtering doesn't work for us (HN)

Effect size is significantly more important than statistical significance (2021) (HN)

How percentile approximation works and why it's more useful than averages (2021) (HN)

Engineering Statistics Handbook