References

Acemoglu, Daron, and Pascual Restrepo. 2018. “Artificial Intelligence, Automation and Work.” National Bureau of Economic Research.

Albert, Jim. 1993. “A Statistical Analysis of Hitting Streaks in Baseball: Comment.” Journal of the American Statistical Association 88 (424): 1184–88. https://www.jstor.org/stable/2291255.

Altić, Mirela Slukan. 2013. “Exploring Along the Rome Meridian: Roger Boscovich and the First Modern Map of the Papal States.” In History of Cartography: International Symposium of the ICA, 2012, 71–89. Springer.

Amazon. 2021. “The History of Amazon’s Forecasting Algorithm.” Amazon Science. https://www.amazon.science/latest-news/the-history-of-amazons-forecasting-algorithm.

Andrews, D. F., and C. L. Mallows. 1974. “Scale Mixtures of Normal Distributions.” Journal of the Royal Statistical Society. Series B (Methodological) 36 (1): 99–102. https://www.jstor.org/stable/2984774.

Arnol’d, Vladimir I. 2006. “Forgotten and Neglected Theories of Poincaré.” Russian Mathematical Surveys 61 (1): 1.

Baylor, Denis, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, et al. 2017. “Tfx: A Tensorflow-Based Production-Scale Machine Learning Platform.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1387–95. ACM.

Behnia, Farnaz, Dominik Karbowski, and Vadim Sokolov. 2021. “Deep Generative Models for Vehicle Speed Trajectories.” arXiv Preprint arXiv:2112.08361.

Benoit, Dries F., and Dirk Van den Poel. 2012. “Binary Quantile Regression: A Bayesian Approach Based on the Asymmetric Laplace Distribution.” Journal of Applied Econometrics 27 (7): 1174–88.

Berge, Travis, Nitish Sinha, and Michael Smolyansky. 2016. “Which Market Indicators Best Forecast Recessions?” FEDS Notes, August.

Bhadra, Anindya, Jyotishka Datta, Nick Polson, Vadim Sokolov, and Jianeng Xu. 2021. “Merging Two Cultures: Deep and Statistical Learning.” arXiv Preprint arXiv:2110.11561.

Bojarski, Mariusz, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, et al. 2016. “End to End Learning for Self-Driving Cars.” arXiv Preprint arXiv:1604.07316. https://arxiv.org/abs/1604.07316.

Boltyanskii, V. G., R. V. Gamkrelidze, and Pontryagin. 1960. “Theory of Optimal Processes i: Maximum Principle.” News of Akad. Nauk SSSR. Mathematics Series 24: 3–42.

Bonfiglio, Rita, Annarita Granaglia, Raffaella Giocondo, Manuel Scimeca, and Elena Bonanno. 2021. “Molecular Aspects and Prognostic Significance of Microcalcifications in Human Pathology: A Narrative Review.” International Journal of Molecular Sciences 22 (120).

Bottou, Léon, Frank E Curtis, and Jorge Nocedal. 2018. “Optimization Methods for Large-Scale Machine Learning.” SIAM Review 60 (2): 223–311.

Bryson, Arthur E. 1961. “A Gradient Method for Optimizing Multi-Stage Allocation Processes.” In Proc. Harvard Univ. Symposium on Digital Computers and Their Applications. Vol. 72.

Campagnoli, Patrizia, Sonia Petrone, and Giovanni Petris. 2009. Dynamic Linear Models with R. New York, NY: Springer.

Cootner, Paul H. 1967. The Random Character of Stock Market Prices. MIT press.

Davison, Anthony Christopher. 2003. Statistical Models. Vol. 11. Cambridge university press.

Dixon, Matthew F, Nicholas G Polson, and Vadim O Sokolov. 2019. “Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading.” Applied Stochastic Models in Business and Industry 35 (3): 788–807.

Dreyfus, Stuart. 1962. “The Numerical Solution of Variational Problems.” Journal of Mathematical Analysis and Applications 5 (1): 30–45.

———. 1973. “The Computational Solution of Optimal Control Problems with Time Lag.” IEEE Transactions on Automatic Control 18 (4): 383–85.

Efron, Bradley, and Carl Morris. 1977. “Stein’s Paradox in Statistics.” Scientific American 236 (5): 119–27.

Enikolopov, Ruben, Vasily Korovkin, Maria Petrova, Konstantin Sonin, and Alexei Zakharov. 2013. “Field Experiment Estimate of Electoral Fraud in Russian Parliamentary Elections.” Proceedings of the National Academy of Sciences 110 (2): 448–52.

Eric Tassone, and Farzan Rohani. 2017. “Our Quest for Robust Time Series Forecasting at Scale.”

Feynman, Richard. n.d. “Feynman :: Rules of Chess.”

Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. 2007. “Auxiliary Mixture Sampling with Applications to Logistic Models.” Computational Statistics & Data Analysis 51 (April): 3509–28.

———. 2010. “Data Augmentation and MCMC for Binary and Multinomial Logit Models.” In Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir, 111–32.

Frühwirth-Schnatter, Sylvia, Rudolf Frühwirth, Leonhard Held, and Håvard Rue. 2008. “Improved Auxiliary Mixture Sampling for Hierarchical Models of Non-Gaussian Data.” Statistics and Computing 19 (4): 479.

Gan, Link, and Alan Fritzler. 2016. “How to Become an Executive.”

Gramacy, Robert B., and Nicholas G. Polson. 2012. “Simulation-Based Regularized Logistic Regression.” arXiv. https://arxiv.org/abs/1005.3430.

Griewank, Andreas, Kshitij Kulshreshtha, and Andrea Walther. 2012. “On the Numerical Stability of Algorithmic Differentiation.” Computing 94 (2-4): 125–49.

Halevy, Alon, Peter Norvig, and Fernando Pereira. 2009. “The Unreasonable Effectiveness of Data.” IEEE Intelligent Systems 24 (2): 8–12.

Hardt, Moritz, Ben Recht, and Yoram Singer. 2016. “Train Faster, Generalize Better: Stability of Stochastic Gradient Descent.” In International Conference on Machine Learning, 1225–34. PMLR.

Heaton, JB, NG Polson, and Jan Hendrik Witte. 2017. “Deep Learning for Finance: Deep Portfolios.” Applied Stochastic Models in Business and Industry 33 (1): 3–12.

Held, Leonhard, and Chris C. Holmes. 2006. “Bayesian Auxiliary Variable Models for Binary and Multinomial Regression.” Bayesian Analysis 1 (1): 145–68.

Hermann, Jeremy, and Mike Del Balso. 2017. “Meet Michelangelo: Uber’s Machine Learning Platform.”

Hyndman, Rob J., and George Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. edition. Melbourne, Australia: Otexts.

indeed. 2018. “Jobs of the Future: Emerging Trends in Artificial Intelligence.”

Irwin, Neil. 2016. “How to Become a C.E.O.? The Quickest Path Is a Winding One.” The New York Times, September.

Januschowski, Tim, Yuyang Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson, and Jan Gasthaus. 2022. “Forecasting with Trees.” International Journal of Forecasting, Special Issue: M5 competition, 38 (4): 1473–81.

kaggle. 2020. “M5 Forecasting - Accuracy.” https://kaggle.com/competitions/m5-forecasting-accuracy.

Keskar, Nitish Shirish, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2016. “On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.” arXiv Preprint arXiv:1609.04836.

Keynes, John Maynard. 1921. A Treatise on Probability. Macmillan.

Kolmogoroff, Andrei. 1931. “Über Die Analytischen Methoden in Der Wahrscheinlichkeitsrechnung.” Mathematische Annalen 104 (1): 415–58.

Kolmogorov, A. N. 1938. “On the Analytic Methods of Probability Theory.” Uspekhi Mat. Nauk, no. 5: 5–41.

Kolmogorov, AN. 1942. “Definition of Center of Dispersion and Measure of Accuracy from a Finite Number of Observations (in Russian).” Izv. Akad. Nauk SSSR Ser. Mat. 6: 3–32.

Kreps, David. 1988. Notes On The Theory Of Choice. Boulder: Westview Press.

Linnainmaa, Seppo. 1970. “The Representation of the Cumulative Rounding Error of an Algorithm as a Taylor Expansion of the Local Rounding Errors.” Master’s Thesis (in Finnish), Univ. Helsinki, 6–7.

Logan, John A. 1983. “A Multivariate Model for Mobility Tables.” American Journal of Sociology 89 (2): 324–49.

Logunov, A. A. 2004. “Henri Poincare and Relativity Theory.” https://arxiv.org/abs/physics/0408077.

Maharaj, Shiva, Nick Polson, and Vadim Sokolov. 2023. “Kramnik Vs Nakamura or Bayes Vs p-Value.” {{SSRN Scholarly Paper}}. Rochester, NY.

Mehrasa, Nazanin, Yatao Zhong, Frederick Tung, Luke Bornn, and Greg Mori. 2017. “Learning Person Trajectory Representations for Team Activity Analysis.” arXiv Preprint arXiv:1706.00893. https://arxiv.org/abs/1706.00893.

Nareklishvili, Maria, Nicholas Polson, and Vadim Sokolov. 2022a. “Deep Partial Least Squares for Iv Regression.” arXiv Preprint arXiv:2207.02612.

———. 2022b. “Feature Selection for Personalized Policy Analysis.” arXiv Preprint arXiv:2301.00251.

———. 2023. “Generative Causal Inference.” arXiv Preprint arXiv:2306.16096.

Nicosia, Luca, Giulia Gnocchi, Ilaria Gorini, Massimo Venturini, Federico Fontana, Filippo Pesapane, Ida Abiuso, et al. 2023. “History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century.” Healthcare 11 (1596).

Ostrovskii, GM, Yu M Volin, and WW Borisov. 1971. “Uber Die Berechnung von Ableitungen.” Wissenschaftliche Zeitschrift Der Technischen Hochschule f Ur Chemie, Leuna-Merseburg 13 (4): 382–84.

Petris, Giovanni. 2010. “An R Package for Dynamic Linear Models.” Journal of Statistical Software 36 (October): 1–16.

Poincaré, Henri. 1898. “La Mesure Du Temps.” Revue de métaphysique Et de Morale 6 (1): 1–13.

Polson, Nicholas G, Vadim Sokolov, et al. 2017. “Deep Learning: A Bayesian Perspective.” Bayesian Analysis 12 (4): 1275–1304.

Polson, Nicholas G, and Vadim Sokolov. 2017. “Deep Learning: A Bayesian Perspective.”

———. 2023. “Generative AI for Bayesian Computation.” arXiv Preprint arXiv:2305.14972.

Polson, Nicholas, and Steven Scott. 2011. “Data Augmentation for Support Vector Machines.” Bayesian Analysis 6 (March).

Polson, Nicholas, and Vadim Sokolov. 2020. “Deep Learning: Computational Aspects.” Wiley Interdisciplinary Reviews: Computational Statistics 12 (5): e1500.

Polson, Nicholas, Vadim Sokolov, and Jianeng Xu. 2021. “Deep Learning Partial Least Squares.” arXiv Preprint arXiv:2106.14085.

Poplin, Ryan, Avinash V Varadarajan, Katy Blumer, Yun Liu, Michael V McConnell, Greg S Corrado, Lily Peng, and Dale R Webster. 2018. “Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning.” Nature Biomedical Engineering 2 (3): 158.

Robbins, Herbert, and Sutton Monro. 1951. “A Stochastic Approximation Method.” The Annals of Mathematical Statistics 22 (3): 400–407. https://doi.org/10.1214/aoms/1177729586.

Rubin, Hal S. Stern, John B. Carlin. 2015. Bayesian Data Analysis. 3rd ed. New York: Chapman and Hall/CRC.

Rumelhart, David E, Geoffrey E Hinton, and Ronald J Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533.

Schmidhuber, Jürgen. 2015. “Deep Learning in Neural Networks: An Overview.” Neural Networks 61: 85–117.

Schwertman, Neil C, AJ Gilks, and J Cameron. 1990. “A Simple Noncalculus Proof That the Median Minimizes the Sum of the Absolute Deviations.” The American Statistician 44 (1): 38–39.

Scott, Steven L. 2015. “Multi-Armed Bandit Experiments in the Online Service Economy.” Applied Stochastic Models in Business and Industry 31 (1): 37–45.

Scott, Steven L. 2022. “BoomSpikeSlab: MCMC for Spike and Slab Regression.”

Scott, Steven L., and Hal R. Varian. 2015. “Bayesian Variable Selection for Nowcasting Economic Time Series.” In Economic Analysis of the Digital Economy, 119–35. University of Chicago Press.

Scott, Steven, and Hal Varian. 2014. “Predicting the Present with Bayesian Structural Time Series.” Int. J. Of Mathematical Modelling and Numerical Optimisation 5 (January): 4–23.

Sean J. Taylor, and Ben Letham. 2017. “Prophet: Forecasting at Scale - Meta Research.” Meta Research. https://research.facebook.com/blog/2017/2/prophet-forecasting-at-scale/.

Shen, Changyu, Enrico G Ferro, Huiping Xu, Daniel B Kramer, Rushad Patell, and Dhruv S Kazi. 2021. “Underperformance of Contemporary Phase III Oncology Trials and Strategies for Improvement.” Journal of the National Comprehensive Cancer Network 19 (9): 1072–78.

Shiryayev, A. N. 1992. “On Analytical Methods in Probability Theory.” In Selected Works of a. N. Kolmogorov: Volume II Probability Theory and Mathematical Statistics, edited by A. N. Shiryayev, 62–108. Dordrecht: Springer Netherlands.

Silver, David, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, et al. 2017. “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.” arXiv. https://arxiv.org/abs/1712.01815.

Simpson, Edward. 2010. “Edward Simpson: Bayes at Bletchley Park.” Significance 7 (2): 76–80.

Sokolov, Vadim. 2017. “Discussion of ’Deep Learning for Finance: Deep Portfolios’.” Applied Stochastic Models in Business and Industry 33 (1): 16–18.

Spiegelhalter, David, and Yin-Lam Ng. 2009. “One Match to Go!” Significance 6 (4): 151–53.

Stein, Charles. 1964. “Inadmissibility of the Usual Estimator for the Variance of a Normal Distribution with Unknown Mean.” Annals of the Institute of Statistical Mathematics 16 (1): 155–60.

Stern, H, Adam Sugano, J Albert, and R Koning. 2007. “Inference about Batter-Pitcher Matchups in Baseball from Small Samples.” Statistical Thinking in Sports, 153–65.

Stigler, Stephen M. 1981. “Gauss and the Invention of Least Squares.” The Annals of Statistics, 465–74.

Sun, Duxin, Wei Gao, Hongxiang Hu, and Simon Zhou. 2022. “Why 90% of Clinical Drug Development Fails and How to Improve It?” Acta Pharmaceutica Sinica B 12 (7): 3049–62.

Taleb, Nassim Nicholas. 2007. The Black Swan: The Impact of the Highly Improbable. Annotated edition. New York. N.Y: Random House.

Tarone, Robert E. 1982. “The Use of Historical Control Information in Testing for a Trend in Proportions.” Biometrics, 215–20.

Tesauro, Gerald. 1995. “Temporal Difference Learning and TD-Gammon.” Communications of the ACM 38 (3): 58–68.

Wang, Yuexi, Nicholas Polson, and Vadim O Sokolov. 2022. “Data Augmentation for Bayesian Deep Learning.” Bayesian Analysis 1 (1): 1–29.

Werbos, Paul. 1974. “Beyond Regression:" New Tools for Prediction and Analysis in the Behavioral Sciences.” Ph. D. Dissertation, Harvard University.

Werbos, Paul J. 1982. “Applications of Advances in Nonlinear Sensitivity Analysis.” In System Modeling and Optimization, 762–70. Springer.

Wojna, Zbigniew, Alex Gorban, Dar-Shyang Lee, Kevin Murphy, Qian Yu, Yeqing Li, and Julian Ibarz. 2017. “Attention-Based Extraction of Structured Information from Street View Imagery.” arXiv Preprint arXiv:1704.03549. https://arxiv.org/abs/1704.03549.