ReadingList

📖 Reading: A community-driven reading initiative. See CONTRIBUTING.md for how to join.

📘 Basic Bayesian

• 1995 — Bayesian Data Analysis — Andrew Gelman, John B. Carlin, Hal S. Stern, et al. Foundational textbook introducing Bayesian modeling and computation. ⭐(1) 📬 Join discussion

• 2002 — Likelihood, Bayesian and MCMC Methods in Quantitative Genetics — Daniel Sorensen, Daniel Gianola, et al. Focused on Bayesian approaches in animal genetics. 🟡 reading 📬 Join discussion

• 2008 — Monte Carlo Statistical Methods — George Casella, Christian Robert Classic on Monte Carlo methods with Bayesian applications. ⭐(1) 🟡 reading 📬 Join discussion

• 2024 — Bayesian Inference: Theory, Methods, Computation, and Applications — Silvelyn Zwanzig, Rauf Ahmad Modern overview of Bayesian methods in theory and application. 🟡 reading 📬 Join discussion

📘 Basic Computational Tools

• 2022 — Bayesian Statistical Modeling with Stan, R, and Python — Kentaro Matsuura A practical guide for implementing Bayesian models using modern probabilistic programming tools. 📬 Join discussion

📘 Basic Genetics

• 2002 — QTL Analysis in Plants — Shizhong Xu Book section in Quantitative Trait Loci: Methods and Protocols. 📬 Join discussion

• 2013 — Principles of Statistical Genomics — Shizhong Xu A foundational reference linking statistics with modern genomic data analysis. 📬 Join discussion

• 2022 — Quantitative Genetics — Shizhong Xu Recent work on classical and modern quantitative genetics methodology. 📬 Join discussion

📘 Basic Linear Algebra

• 2003 — Linear Algebra and Its Applications — David C. Lay Clear explanations with real-world examples and exercises. 📬 Join discussion

• 2012 — Topics in Random Matrix Theory — Terence Tao A graduate-level treatment of probabilistic aspects of matrix theory. 📬 Join discussion

• 2022 — Numerical Linear Algebra — Lloyd N. Trefethen, David Bau Practical algorithms and numerical stability insights. 📬 Join discussion

• 2022 — Introduction to Linear Algebra — Gilbert Strang Intuitive and application-driven textbook widely used in undergraduate courses. 📬 Join discussion

• 2024 — Linear Algebra Done Right — Sheldon Axler Abstract and elegant approach focusing on linear maps. 📬 Join discussion

📘 Basic Probability Theory

• 1983 — Probability and Measure (by P. Billingsley) — Gavin Brown A review published in the Proceedings of the Edinburgh Mathematical Society. 📬 Join discussion

• 1991 — Probability with Martingales — David Williams A foundational graduate-level text introducing measure-theoretic probability. 📬 Join discussion

• 2008 — Introduction to Probability — Dimitri Bertsekas, John N. Tsitsiklis MIT-style course material with elegant explanations and structure. 📬 Join discussion

• 2013 — All of Statistics: A Concise Course in Statistical Inference — Larry Wasserman A fast-paced introduction to probability and statistics for machine learning and statistics students. 📬 Join discussion

• 2020 — A First Course in Probability — Sheldon M. Ross Widely adopted textbook known for its clarity and comprehensive coverage. 📬 Join discussion

📘 Basic Real Analysis

• 2004 — Real Mathematical Analysis (by Charles Chapman Pugh) — Gerry Leversha Review published in The Mathematical Gazette.

• 2006 — Analysis — Terence Tao Volume I of Tao's celebrated analysis series, rigorous and accessible. 🟡 reading

• 2015 — Understanding Analysis — Stephen Abbott Intuitive and student-friendly text, often used as a stepping stone to Rudin.

• 2016 — Analysis II — Terence Tao Continuation of Tao's undergraduate analysis text, covering multivariable analysis.

• 2020 — Measure, Integration & Real Analysis — Sheldon Axler A detailed, measure-theoretic approach to real analysis.

• 2021 — Principles of Mathematical Analysis — Walter Rudin The "Baby Rudin" classic — compact, rigorous, and essential for serious study.

📘 Advanced Calculus

• 2003 — Advanced Calculus with Applications in Statistics Published in Technometrics.

• 2016 — Applied Matrix Algebra in the Statistical Sciences — Brigitte Maier Conference proceeding.

📘 Advanced Complex Analysis

• 2009 — Complex Variables and Applications — James Ward Brown Classic textbook balancing theory and application.

• 2010 — Complex Analysis — Elias M. Stein, Rami Shakarchi A rigorous and modern take from the Princeton Lectures in Analysis series.

• 2012 — Functions of One Complex Variable II — John B. Conway Graduate-level continuation with deeper results.

• 2023 — Visual Complex Analysis — Tristan Needham Geometric and intuitive approach to complex function theory.

📘 Advanced Functional Analysis

• 1991 — Introductory Functional Analysis with Applications — Erwin Kreyszig Application-focused introduction, popular in applied fields.

• 1994 — A Course in Functional Analysis — John B. Conway A widely-used graduate text with strong theoretical depth.

• 2000 — Functional Analysis — George Bachman Covers fundamental concepts with accessible exposition.

• 2013 — A Guide to Functional Analysis: Preface — Steven G. Krantz Short and insightful, useful for orientation and review.

• 2023 — Functional Analysis — D. A. Brannan, M. F. Esplin Modern text; part of Texts and Readings in Mathematics.

📘 Advanced Matrix

• 2016 — Applied Matrix Algebra in the Statistical Sciences — Brigitte Maier Conference proceeding.

• 2018 — Linear Models and the Relevant Distributions and Matrix Algebra — David A. Harville Focused on statistical applications and linear theory.

• 1983 — Matrices with Applications in Statistics — Frank A. Graybill Classic reference in multivariate statistical modeling.

• 1988 — Numerical Analysis: 4th Ed. — Richard L. Burden Numerical linear algebra methods in a classic textbook.

• 1998 — Matrix Algebra and Its Applications to Statistics and Econometrics — Lorens A. Imhof Published in Journal of the American Statistical Association. 🟡 reading

• 1998 — Matrix Algebra from a Statistician's Perspective — David A. Harville Detailed, technical, and statistically grounded.

• 2006 — Matrix Differentiation — Randal J. Barnes Technical reference for derivatives involving matrices.

• 2007 — A Matrix Handbook for Statisticians — George A. F. Seber Useful matrix identities, especially for applied contexts.

• 2007 — Matrix Algebra: Theory, Computations, and Applications in Statistics — James E. Gentle Comprehensive treatment with R examples.

• 2011 — Matrix Computations — Andrzej Chrzeszczyk Encyclopedia-style coverage of algorithms and theory.

• 2012 — Matrix Analysis — Roger A. Horn, Charles R. Johnson Rigorous and advanced, widely regarded in pure and applied contexts.

• 2016 — Matrix-Based Introduction to Multivariate Data Analysis — Kohei Adachi Step-by-step development from matrix theory to multivariate stats.

• 2017 — Matrix Algebra Useful for Statistics — Shayle R. Searle Practical and computational focus with statistical orientation.

• 2023 — The Art of Linear Algebra — Kenji Hiranabe A visual and intuitive approach to matrix theory.

• 2023 — The Dark Art of Linear Algebra: An Intuitive Geometry-Based Approach — Seth Braver Emphasizes geometry and understanding over formalism.

📘 Advanced Tensor

• 1997 — A Brief on Tensor Analysis — James G. Simmonds Concise and approachable introduction to tensor calculus.

• 2009 — Tensor Decompositions and Applications — Tamara G. Kolda, Brett W. Bader Survey article published in SIAM Review; foundational for tensor methods.

• 2012 — Tensor Analysis on Manifolds — Richard L. Bishop, Samuel I. Goldberg Rigorous approach to differential geometry with tensor tools.

• 2013 — Introduction to Tensor Analysis and the Calculus of Moving Surfaces — Pavel Grinfeld Accessible treatment with physical intuition.

• 2018 — Tensor Methods in Statistics: Monographs on Statistics and Applied Probability — Peter McCullagh Advanced statistical modeling using tensor structures.

📘 Major Statistical Inference

• 1995 — Bayesian Data Analysis — Andrew Gelman, John B. Carlin, Hal S. Stern, et al. The standard graduate text in Bayesian methods.

• 1995 — Logistic Regression: A Self-Learning Text — Farid Kianifard Concept-focused entry to logistic models.

• 1996 — Survival Analysis: A Self-Learning Text — David G. Kleinbaum, Mitchel Klein Applied perspective with biomedical examples.

• 1997 — Statistics and Truth: Putting Chance to Work — Calyampudi R. Rao A philosophical and practical view of statistical modeling.

• 2006 — Pattern Recognition and Machine Learning — Christopher M. Bishop, Nassir Navab Canonical reference for statistical machine learning.

• 2010 — Theoretical Statistics: Topics for a Core Course — Robert W. Keener Advanced topics in statistical theory, especially for PhD qualifiers.

• 2010 — The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani, Jerome Friedman Canonical reference for statistical learning theory.

• 2011 — The Fundamentals of Modern Statistical Genetics — Nan M. Laird, Christoph Lange Focused on genetic association studies and quantitative trait analysis.

• 2012 — Probability and Statistics, Fourth Edition — Morris H. DeGroot, Mark J. Schervish Classic and comprehensive undergraduate text.

• 2012 — Topics in Random Matrix Theory — Terence Tao Analytical tools for inference in high-dimensional spaces.

• 2013 — All of Statistics: A Concise Course in Statistical Inference — Larry Wasserman A unified introduction to probability and inference for modern data science.

• 2015 — Applied Multivariate Statistics with R — Daniel Zelterman Emphasises practical multivariate methods using R.

• 2015 — A Mathematical Introduction to Compressive Sensing — 慧穂 廣瀬 Article published in 日本统计学会讲经 (The Journal of the Japanese Statistical Society).

• 2019 — High-Dimensional Statistics — Martin J. Wainwright Non-asymptotic methods for large-scale statistical models.

• 2020 — Likelihood and Bayesian Inference — Leonhard Held, Daniel Sabanés Bové Journal article published in Statistics for Biology and Health.

• 2024 — Statistical Inference — George Casella, Roger Berger A rigorous, widely-used standard graduate-level textbook.

📘 Mathematical Logic

• 1986 — How to Prove a Theorem So No One Else Can Claim It — Manuel Blum Conference paper exploring originality in proof construction (Proceedings of the International Congress of Mathematicians).

• 1989 — Proofs That Prove and Proofs That Explain — Gila Hanna On the role of explanatory versus deductive proofs in mathematics education (Proceedings of the International Conference on the Psychology of Mathematics Education).

• 1999 — Why Do We Prove Theorems? — Yehuda Rav Philosophical reflection published in Philosophia Mathematica.

• 2002 — Learning to Prove: The Idea of Heuristic Examples — Kristina Reiss, Alexander Renkl Published in Zentralblatt für Didaktik der Mathematik, emphasising teaching strategy.

• 2005 — Learning to Prove in Order to Prove to Learn — Jessica Knapp Research article on pedagogical feedback loops in proof education.

• 2012 — Logicomix: An Epic Search for Truth — Christian Robert Graphic novel blending storytelling and foundational logic.

• 2019 — How to Prove It: A Structured Approach — Daniel J. Velleman Widely used textbook that teaches logic and proof techniques.

Contributing

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