# Statistics

Probabilistic Machine Learning: Advanced Topics, Statistical Rethinking & Elements of Statistical Learning are great reads.

High-Dimensional Probability is nice read too.

## Notes

## Links

- The Probability and Statistics Cookbook (HN)
- Ask HN: What are your favorite statistics and probability textbooks? (2018)
- Statistics with R Specialization course
- Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence (HN) (Code)
- 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 (HN) (HN)
- 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
- posteriordb - Database with posteriors of interest for Bayesian inference.
- Simplest example of Simpson's paradox (2021) (Lobsters)
- Complex Systems: a Physicist's Viewpoint (2021)
- How to replace estimations and guesses with a Monte Carlo simulation (2021) (HN) (Code)
- Bayesian Methods Research Group (Twitter)
- Awesome Bayesian Statistics
- Bayesian histograms for rare event classification (2021)
- Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials (2021) (Tweet)
- The Non-parametric Bootstrap as a Bayesian Model (2015)
- Prediction, Estimation, and Attribution (2019) (Tweet)
- Probability and Statistics Cheat Sheet (2021)
- Bayesian Optimization Book (2021) (Code) (HN)
- Monte Carlo Methods or Why It's a Bad Idea to Go to the Casino (HN)
- Statistical Rethinking Course (2022) (Videos) (HN) (HN) (Code)
- GMS introductory course in statistics
- What's the difference between stochastic and random?
- Twelve problems in probability no one likes to bring up
- An Introduction to Probability Theory and Its Applications, Volume 1 by William Feller (1991)
- A New Coefficient of Correlation (2019) (HN)
- Ask HN: What is your favorite book for learning statistics? (2021)
- Visualizing Bayes Theorem (2009)
- Dependent probabilities - Interactive visualization to explore dependent probabilities in terms of area and side length of rectangles.
- Introduction to Probability for Data Science (HN)
- Regression and Other Stories
- The Kelly criterion: How to size bets
- What is antifragility?
- Fisher–Tippett–Gnedenko theorem - Wikipedia - There are only three possible asymptotic forms of extreme order statistics.
- Bayesian inference of phylogeny is robust to substitution model over-parameterization (2022) (Tweet)
- The Kalman Filter. Helping Chickens Cross the Road (2022) (HN)
- Basic Modeling for Discrete Optimization (Tweet)
- Regression, a Friendly Guide Book (2022) (Code)
- Improving forecasting by learning quantile functions (2022)
- Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
- Truth and Probability by Frank P. Ramsey (1926) (Tweet)
- Good books on statistics (2022)
- rethinking - Statistical Rethinking course and book package.
- Topics In Modern Statistical Learning (UPenn, 2022 Spring)
- Kelly criterion - Wikipedia
- Bayesian Statistics graduate course
- Bayes Rules – An Introduction to Applied Bayesian Modeling (2021)](https://www.bayesrulesbook.com/) (HN)
- Consistency of invariance-based randomization tests (2021)
- StockBot: Using LSTMs to Predict Stock Prices (2022)
- Modeling Mindsets: The Many Cultures of Learning From Data (2022) (Code)
- Monte Carlo Geometry Processing (2022)
- Veri - Distributed Feature Store optimized for Search and Recommendation tasks.
- Transport Elliptical Slice Sampling (2022) (Code)
- Making sense of principal component analysis, eigenvectors & eigenvalues
- The Exponential Family - YouTube
- Statistical Process Control: A Manager’s Guide
- ICLR 2023 Statistics (Code)
- Understanding Convolutions in Probability (2022) (HN)
- Mean (μ) and median (m) are within a std deviation (σ)
- Statistical Rethinking Course (2023)
- High-Dimensional Probability and Applications in Data Science
- Applied Stochastic Differential Equations (2019)
- Stochastic Differential Equations: Introduction with Applications
- Probabilistic Filters By Example: Cuckoo Filter and Bloom Filters
- Much Ado About Sampling (2022)
- Learning Statistics Shiny App
- The Unreasonable Effectiveness of Conditional Probabilities (2023) (HN)
- Curated Book List - Open Access Textbooks on Statistics
- A User’s Guide to Statistical Inference and Regression (Code)
- Statistics Handbook
- K-Means Clustering: An Explorable Explainer (Code)
- Statistical Arbitrage – An Easy Walkthrough (2023) (HN)
- It Takes Long to Become Gaussian (2023) (HN)
- Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction (2023) (Code)