Instructions

If you cite The Book of Statistical Proofs in your scientific work, it is best practice to reference the Zenodo DOI (10.5281/zenodo.4305949) which always resolves the latest version of the StatProofBook. This could e.g. look as follows:

Alternatively, you can also directly cite a proof from the archive in your article:

Alternatively, you may also include a footnote with the URL into your article:


Hall of Fame

Here is a list of scientific articles that have so far cited content from The Book of Statistical Proofs:

  • Awad, P., Chan, J. H. H., Millon, M., Courbin, F., & Paic, E. (2023a). Probing compact dark matter objects with microlensing in gravitationally lensed quasars. Astronomy & Astrophysics, 673, A88. https://doi.org/10.1051/0004-6361/202245615

  • Awad, P., Chan, J. H. H., Millon, M., Courbin, F., & Paic, E. (2023b). Probing compact dark matter objects with microlensing in gravitationally lensed quasars. https://doi.org/10.48550/ARXIV.2304.01320

  • Bilton, M. A. (2022). Use of Surrogate Models for Continuous Optimal Experimental Design [Thesis, The University of Auckland]. https://researchspace.auckland.ac.nz/handle/2292/61541

  • Cervati Neto, A., Levada, A. L. M., & Ferreira Cardia Haddad, M. (2024). Supervised t-SNE for Metric Learning With Stochastic and Geodesic Distances. IEEE Canadian Journal of Electrical and Computer Engineering, 47(4), 199–205. https://doi.org/10.1109/ICJECE.2024.3429273

  • Chung, E., Na, S., Kang, S. H., & Kim, H. (2025). Swift and Trustworthy Large-Scale GPU Simulation with Fine-Grained Error Modeling and Hierarchical Clustering. Proceedings of the 58th IEEE/ACM International Symposium on Microarchitecture, 1397–1411. https://doi.org/10.1145/3725843.3757107

  • Coupechoux, J.-F., Chierici, R., Hansen, H., Margueron, J., Somasundaram, R., & Sordini, V. (2023). Impact of O4 future detections on the determination of the dense matter equations of state. Physical Review D, 107(12), 124006. https://doi.org/10.1103/PhysRevD.107.124006

  • Dam, T., Stenger, P., Schneider, L., Pajarinen, J., D’Eramo, C., & Maillard, O.-A. (2023). Monte-Carlo tree search with uncertainty propagation via optimal transport. https://doi.org/10.48550/ARXIV.2309.10737

  • de la Torre, J. (2023). Autocodificadores Variacionales (VAE) Fundamentos Teóricos y Aplicaciones. https://doi.org/10.48550/ARXIV.2302.09363

  • Děd, T. (2023). Konstrukce modelu pro překlad záznamu znakového jazyka s využitím neuronových sítí. https://dspace.cvut.cz/handle/10467/111309

  • Duran-Martin, G. (2025). Adaptive, Robust and Scalable Bayesian Filtering for Online Learning (No. arXiv:2505.07267). arXiv. https://doi.org/10.48550/arXiv.2505.07267

  • Fajar, M., & Nariswari, R. (2026). On the New Modification of Schwarz Information Criterion. In Y. B. Wah, D. Al-Jumeily, & M. W. Berry (Eds.), Data Science and Emerging Technologies (Vol. 257, pp. 221–233). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-7749-8_15

  • Fajar, M., Setiawan, & Iriawan, N. (2023). The Adjusted SNR and It’s Application for Selection Lorenz Function of Income Inequality Analysis. Procedia Computer Science, 227, 1–16. https://doi.org/10.1016/j.procs.2023.10.497

  • Görner, M., Dicke, P. W., & Thier, P. (2023). Is there a brain area dedicated to socially guided spatial attention? [Preprint]. Neuroscience. https://doi.org/10.1101/2023.01.20.524674

  • Hansen, A. B., & Jepsen, S. D. (2024). A Benchmarking Tool for Evaluation of Approximate Arithmetic Circuits in Convolutional Neural Networks [Master’s Thesis, Aalborg University]. https://projekter.aau.dk/a-benchmarking-tool-for-evaluation-of-approximate-arithmetic-circuits-in-convolutional-neural-networ-21ddf6f1.html

  • Hashemi-Alavi, P. (2024). Variable selection for variable length Markov chains with exogenous covariates (Cal State). California State University, Northridge.

  • Heußen, S. H. (2024). Applications of fault-tolerant topological quantum error correction in near-term devices [RWTH Aachen University]. In Dissertation: Vol. RWTH Aachen University (p. pages 1 Online-Ressource : Illustrationen). https://doi.org/10.18154/RWTH-2024-00957

  • Heußen, S., Winter, D., Rispler, M., & Müller, M. (2023). Dynamical subset sampling of quantum error correcting protocols. https://doi.org/10.48550/ARXIV.2309.12774

  • Heußen, S., Winter, D., Rispler, M., & Müller, M. (2024). Dynamical subset sampling of quantum error-correcting protocols. Physical Review Research, 6(1), 013177. https://doi.org/10.1103/PhysRevResearch.6.013177

  • Hui, X., Qu, H., Rahmani, H., & Liu, J. (2025a). An Image-like Diffusion Method for Human-Object Interaction Detection (No. arXiv:2503.18134). arXiv. https://doi.org/10.48550/arXiv.2503.18134

  • Hui, X., Qu, H., Rahmani, H., & Liu, J. (2025b). An Image-like Diffusion Method for Human-Object Interaction Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14002–14012.

  • Ivănescu, L., & O’Neill, N. T. (2023). Multi-star calibration in starphotometry. Atmospheric Measurement Techniques, 16(24), 6111–6121. https://doi.org/10.5194/amt-16-6111-2023

  • John N. Mlyahilu & Kim Jong Nam. (2022). Similarity Measurement Between Titles and Abstracts Using Bijection Mapping and Phi-Correlation Coefficient. 융합신호처리학회 논문지, 23(3), 143–149. DBpia.

  • Kouw, W. M. (2024). Information-Seeking Polynomial NARX Model-Predictive Control Through Expected Free Energy Minimization. IEEE Control Systems Letters, 8, 37–42. https://doi.org/10.1109/LCSYS.2023.3347190

  • Lai, C.-Y., Ning, Y.-C., & Boning, D. S. (2025). RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting (No. arXiv:2509.02341). arXiv. https://doi.org/10.48550/arXiv.2509.02341

  • Lande, C. R., Iriawan, N., & Prastyo, D. D. (2024). Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data. Procedia Computer Science, 245, 490–499. https://doi.org/10.1016/j.procs.2024.10.275

  • Lande, C. R., Iriawan, N., & Prastyo, D. D. (2025). On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data. MethodsX, 14, 103095. https://doi.org/10.1016/j.mex.2024.103095

  • Larsen, A. H. (2023). Fitting multiple small-angle scattering datasets simultaneously: On the optimal use of priors and weights. https://doi.org/10.48550/ARXIV.2311.06408

  • Liu, X., Yuan, J., An, B., Xu, Y., Yang, Y., & Huang, F. (2023). C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder. https://doi.org/10.48550/ARXIV.2310.17325

  • Loukas, O., & Chung, H. R. (2023). Total Empiricism: Learning from Data. https://doi.org/10.48550/ARXIV.2311.08315

  • Morrison, E. (2026, January 15). Regression Models for Epidemiology [Lecture Notes]. GitHub. https://d-morrison.github.io/rme/Regression-Models-for-Epidemiology.pdf

  • Mulder, E. (2023). Fast square-free decomposition of integers using class groups. https://doi.org/10.48550/ARXIV.2308.06130

  • Muniasamy, A., & Alasmari, A. (2025). Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images. Computer Modeling in Engineering & Sciences, 143(1), 569–592. https://doi.org/10.32604/cmes.2025.060484

  • Mustapa, N. A., Senawi, A., & Liang, C. Z. (2023). Feature Selection Using Law of Total Variance with Fast Correlation-Based Filter. 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), 35–40. https://doi.org/10.1109/ICSECS58457.2023.10256367

  • Mustapa, N. A., Senawi, A., & Wei, H.-L. (2023). Supervised Feature Selection based on the Law of Total Variance. MEKATRONIKA, 5(2), 100–110. https://doi.org/10.15282/mekatronika.v5i2.9998

  • Öz, H. N. (2023). New Risk Measures: Magnitude and Propensity Approach [Master Thesis, University of Padua]. https://thesis.unipd.it/handle/20.500.12608/52276

  • Özkaya, E., Rottmayer, J., & Gauger, N. R. (2024). Gradient Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization. In AIAA SCITECH 2024 Forum. https://doi.org/10.2514/6.2024-2670

  • Rizqiansyah, A., & Caprani, C. C. (2026). On the upper bound of the distribution of bridge traffic loading. Reliability Engineering & System Safety, 265, 111463. https://doi.org/10.1016/j.ress.2025.111463

  • Santi, L., & Friel, N. (2025). The Bradley-Terry Stochastic Block Model (No. arXiv:2511.03467). arXiv. https://doi.org/10.48550/arXiv.2511.03467

  • Scham, M. (with Borras, K., Krämer, M., Kasieczka, G., & Scham, M.). (2025). Development of a Tree-Based Model for the Fast Generation of Large Point Clouds Representing Particle Showers in Calorimeters. In Dissertation: Vol. RWTH Aachen University (p. pages 233). Deutsches Elektronen-Synchrotron, DESY, Hamburg. https://doi.org/10.3204/PUBDB-2025-00811

  • Šimon, S. (2022). Metody návrhu experimentů pro tvorbu zjednodušeného modelu okraje plasmatu [B.S. thesis, České vysoké učení technické v Praze. Vypočetní a informační centrum.]. https://dspace.cvut.cz/handle/10467/101041

  • Smith, I., Ortmann, J., Abbas-Aghababazadeh, F., Smirnov, P., & Haibe-Kains, B. (2023). On the distribution of cosine similarity with application to biology. https://doi.org/10.48550/ARXIV.2310.13994

  • Soch, J. (2020). Distributional Transformation Improves Decoding Accuracy When Predicting Chronological Age From Structural MRI. Frontiers in Psychiatry, 11, 604268. https://doi.org/10.3389/fpsyt.2020.604268

  • Soch, J. (2023). Searchlight-based trial-wise fMRI decoding in the presence of trial-by-trial correlations [Preprint]. Neuroscience. https://doi.org/10.1101/2023.12.05.570090

  • Soch, J. (2025). Searchlight-based trial-wise fMRI decoding in the presence of trial-by-trial correlations. Imaging Neuroscience, 3, IMAG.a.131. https://doi.org/10.1162/IMAG.a.131

  • Soch, J., & Allefeld, C. (2025). Multivariate Bayesian Inversion for Classification and Regression. Neuroscience. https://doi.org/10.1101/2025.05.09.653015

  • Soch, J., Richter, A., Schott, B. H., & Kizilirmak, J. M. (2022). A novel approach for modelling subsequent memory reports by separating decidedness, recognition and confidence [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/u5t82

  • Spiess, A.-N., Rödiger, S., Schaks, M., Burdukiewicz, M., & Tellinghuisen, J. (2024). Scientific reasoning driven by influential data: Resuscitate dfstat! Scientific Communication and Education. https://doi.org/10.1101/2024.10.30.621016

  • Subramonian, A., Sagun, L., Chang, K.-W., & Sun, Y. (2022). Group Excess Risk Bound of Overparameterized Linear Regression with Constant-Stepsize SGD. Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022. https://openreview.net/forum?id=TRpJAAK3o0X

  • Tzai, L., Ntzoufras, I., & Bozza, S. (2026). Bayesian Handwriting Evidence Evaluation using MANOVA via Fourier-Based Extracted Features (No. arXiv:2601.07534). arXiv. https://doi.org/10.48550/arXiv.2601.07534

  • Vinaroz, M., & Park, M. (2021). Differentially private stochastic expectation propagation (DP-SEP). https://doi.org/10.48550/ARXIV.2111.13219

  • Vinaroz, M., & Park, M. (2022). Differentially Private Stochastic Expectation Propagation. Transactions on Machine Learning Research. https://openreview.net/forum?id=e5ILb2Nqst

  • Vonwirth, P., Sivak, O., & Berns, K. (2024). Foundations of Probabilistic Behavior Networks Aiming for Structured, Distributed Control of Complex Systems like Legged Robots. 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 593–598. https://doi.org/10.1109/BioRob60516.2024.10719847

  • Wiedner, W. (2024). Variational Inference for Bayesian Mixture Models with a Random Number of Components [Diploma Thesis, Technische Universität Wien]. https://doi.org/10.34726/HSS.2024.107561

  • Wood, J. S., & Gayah, V. (2025). Out-of-sample prediction and interpretation for random parameter generalized linear models. Accident Analysis & Prevention, 220, 108147. https://doi.org/10.1016/j.aap.2025.108147

  • Zeng, H., Lyu, H., Hu, D., Xia, Y., & Luo, J. (2023). Mixture of Weak & Strong Experts on Graphs. https://doi.org/10.48550/ARXIV.2311.05185

  • Zhang, H., Liu, X., Altaf, F., & Wik, T. (2025). A Practitioner’s Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications (No. arXiv:2505.01674). arXiv. https://doi.org/10.48550/arXiv.2505.01674

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