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Bibliography

Primary sources behind the book's theory, grouped by theme. Each entry carries an explicit anchor so any chapter can link directly to it (for example bibliography.md#cox1957). Full bibliographic data and the committed PDFs live in papers/README.md in the repository; publisher-only papers are listed here with a DOI instead of a local copy.

Optimal design

  • Kiefer, J., & Wolfowitz, J. (1960). The equivalence of two extremum problems. Canadian Journal of Mathematics, 12, 363–366. doi:10.4153/CJM-1960-030-4. Origin of the D-/G-optimality equivalence theorem.
  • Whittle, P. (1973). Some general points in the theory of optimal experimental design. Journal of the Royal Statistical Society: Series B, 35(1), 123–130. doi:10.1111/j.2517-6161.1973.tb00944.x.
  • Näther, W., & Reinsch, V. (1981). \(D_s\)-optimality and Whittle's equivalence theorem. Series Statistics, 12(3), 307–316. doi:10.1080/02331888108801591.
  • Pukelsheim, F. (2006). Optimal Design of Experiments. SIAM Classics in Applied Mathematics. doi:10.1137/1.9780898719109.

Quantization for estimation

  • Ogawa, J. (1951). Contributions to the theory of systematic statistics, I. Osaka Mathematical Journal, 3(2), 175–213. Optimal spacings of order statistics maximizing retained Fisher information — the asymptotic 1D ancestor of hard-label quantization for estimation.
  • Cox, D. R. (1957). Note on grouping. Journal of the American Statistical Association, 52(280), 543–547. doi:10.1080/01621459.1957.10501411. Early direct prior art: choosing a 1D grouping to minimize information loss.
  • Max, J. (1960). Quantizing for minimum distortion. IRE Transactions on Information Theory, 6(1), 7–12. doi:10.1109/TIT.1960.1057548.
  • Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. Bell Labs memorandum, 1957. doi:10.1109/TIT.1982.1056489.
  • Gray, R. M., & Neuhoff, D. L. (1998). Quantization. IEEE Transactions on Information Theory, 44(6), 2325–2383. doi:10.1109/18.720541. Canonical survey of quantization theory.
  • Tsitsiklis, J. N. (1993). Extremal properties of likelihood-ratio quantizers. IEEE Transactions on Communications, 41(4), 550–558. doi:10.1109/26.223779. Detection-side analogue: sufficiency of likelihood-ratio space for quantizer design.
  • Venkitasubramaniam, P., Tong, L., & Swami, A. (2006). Score-function quantization for distributed estimation. In Conference on Information Sciences and Systems (CISS). doi:10.1109/CISS.2006.286494.
  • Farias, R. C., & Brossier, J.-M. (2013). Optimal scalar quantization for parameter estimation. arXiv:1310.6945.
  • Barnes, L. P., Han, Y., & Özgür, A. (2018). A geometric characterization of Fisher information from quantized samples with applications to distributed statistical estimation. In Allerton Conference on Communication, Control, and Computing. doi:10.1109/ALLERTON.2018.8635899.
  • Barnes, L. P., Han, Y., & Özgür, A. (2020). Lower bounds for learning distributions under communication constraints via Fisher information. Journal of Machine Learning Research, 21(236), 1–30. arXiv:1902.02890. Extends the geometric characterization above from a conference note to the full journal treatment.
  • Dülek, B. (2023). On the optimality of sufficient statistics-based quantizers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3567–3573. doi:10.1109/TPAMI.2022.3172282.

Clustering

  • Friedman, H. P., & Rubin, J. (1967). On some invariant criteria for grouping data. Journal of the American Statistical Association, 62(320), 1159–1178. doi:10.1080/01621459.1967.10500923.
  • Scott, A. J., & Symons, M. J. (1971). Clustering methods based on likelihood ratio criteria. Biometrics, 27(2), 387–397. doi:10.2307/2529003.
  • Marriott, F. H. C. (1971). Practical problems in a method of cluster analysis. Biometrics, 27(3), 501–514. doi:10.2307/2528592.
  • Hartigan, J. A. (1975). Clustering Algorithms. Wiley.
  • Pollard, D. (1981). Strong consistency of k-means clustering. Annals of Statistics, 9(1), 135–140. doi:10.1214/aos/1176345339.
  • Inaba, M., Katoh, N., & Imai, H. (1994). Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. In Symposium on Computational Geometry (SoCG). doi:10.1145/177424.178042.
  • Du, Q., Faber, V., & Gunzburger, M. (1999). Centroidal Voronoi tessellations: applications and algorithms. SIAM Review, 41(4), 637–676. doi:10.1137/S0036144599352836.
  • Du, Q., Emelianenko, M., & Ju, L. (2006). Convergence of the Lloyd algorithm for computing centroidal Voronoi tessellations. SIAM Journal on Numerical Analysis, 44(1), 102–119. doi:10.1137/040617364.
  • Telgarsky, M., & Vattani, A. (2010). Hartigan's method: k-means clustering without Voronoi. In AISTATS, PMLR 9, 820–827.

Simulation-based inference and inference-aware binning

  • Cranmer, K., Pavez, J., & Louppe, G. (2015). Approximating likelihood ratios with calibrated discriminative classifiers. arXiv:1506.02169.
  • Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., & Kawanabe, M. (2008). Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics, 60(4), 699–746. doi:10.1007/s10463-008-0197-x.
  • Kanamori, T., Hido, S., & Sugiyama, M. (2009). A least-squares approach to direct importance estimation. Journal of Machine Learning Research, 10, 1391–1445. jmlr.org/papers/v10/kanamori09a.
  • Brehmer, J., Louppe, G., Pavez, J., & Cranmer, K. (2020). Mining gold from implicit models to improve likelihood-free inference. Proceedings of the National Academy of Sciences, 117(10), 5242–5249. arXiv:1805.12244.
  • de Castro, P., & Dorigo, T. (2019). INFERNO: inference-aware neural optimisation. Computer Physics Communications, 244, 170–179. arXiv:1806.04743.
  • Matchev, K. T., & Shyamsundar, P. (2021). Optimal event selection and categorization in high energy physics. Part I: Signal discovery. Journal of High Energy Physics, 03, 291. arXiv:1911.12299.
  • Erdmann, J., Kasaraguppe, N. K., & Mausolf, F. (2026). Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physics. arXiv:2601.07756.

Classical decision theory

  • Dvoretzky, A., Wald, A., & Wolfowitz, J. (1951). Elimination of randomization in certain statistical decision procedures and zero-sum two-person games. Annals of Mathematical Statistics, 22(1), 1–21. doi:10.1214/aoms/1177729689.
  • Khan, M. A., Rath, K. P., & Sun, Y. (2006). The Dvoretzky–Wald–Wolfowitz theorem and purification in atomless finite-action games. International Journal of Game Theory, 34, 91–104. doi:10.1007/s00182-005-0004-3. Modern measure-theoretic statement of the purification result the book quotes as established mathematics, not as a novel contribution.