Home Statistics and Visualization Stats Chat Progress in 2023, Aki Edition

Progress in 2023, Aki Edition

Stats Chat 2024-01-15

Following Andrew, here is my (Aki’s) list of published papers and preprints in 2023 (20% together with Andrew)

Published

  1. Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, and Jonathan H. Huggins (2023). Robust, Automated, and Accurate Black-box Variational Inference. Journal of Machine Learning Research, accepted for publication. arXiv preprint arXiv:2203.15945.

  2. Alex Cooper, Dan Simpson, Lauren Kennedy, Catherine Forbes, and Aki Vehtari (2023). Cross-validatory model selection for Bayesian autoregressions with exogenous regressors. Bayesian Analysis, accepted for publication. arXiv preprint arXiv:2301.08276.

  3. Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, and Aki Vehtari (2023). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Statistics and Computing, 34(57). Online arXiv preprint arXiv:2107.14054. Supplementary code. priorsense: R package

  4. Martin Modrák, Angie H. Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, and Aki Vehtari (2023). Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity. Bayesian Analysis, doi:10.1214/23-BA1404. arXiv preprint arXiv:2211.02383. Code SBC R package

  5. Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, and Aki Vehtari (2023). Past, Present, and Future of Software for Bayesian Inference. Statistical Science, accepted for publication. preprint

  6. Marta Kołczyńska, Paul-Christian Bürkner, Lauren Kennedy, and Aki Vehtari (2023). Trust in state institutions in Europe, 1989–2019. Survey Research Methods, accetped for publication. SocArXiv preprint doi:10.31235/osf.io/3v5g7.

  7. Juho Timonen, Nikolas Siccha, Ben Bales, Harri Lähdesmäki, and Aki Vehtari (2023). An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models. Stat, doi:10.1002/sta4.614. arXiv preprint arXiv:2205.09059.

  8. Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Bürkner, Arto Klami (2023). Prior knowledge elicitation: The past, present, and future. Bayesian Analysis, doi:10.1214/23-BA1381. arXiv preprint arXiv:2112.01380.

  9. Peter Mikula, Oldřich Tomášek, Dušan Romportl, Timothy K. Aikins, Jorge E. Avendaño, Bukola D. A. Braimoh-Azaki, Adams Chaskda, Will Cresswell, Susan J. Cunningham, Svein Dale, Gabriela R. Favoretto, Kelvin S. Floyd, Hayley Glover, Tomáš Grim, Dominic A. W. Henry, Tomas Holmern, Martin Hromada, Soladoye B. Iwajomo, Amanda Lilleyman, Flora J. Magige, Rowan O. Martin, Marina F. de A. Maximiano, Eric D. Nana, Emmanuel Ncube, Henry Ndaimani, Emma Nelson, Johann H. van Niekerk, Carina Pienaar, Augusto J. Piratelli, Penny Pistorius, Anna Radkovic, Chevonne Reynolds, Eivin Røskaft, Griffin K. Shanungu, Paulo R. Siqueira, Tawanda Tarakini, Nattaly Tejeiro-Mahecha, Michelle L. Thompson, Wanyoike Wamiti, Mark Wilson, Donovan R. C. Tye, Nicholas D. Tye, Aki Vehtari, Piotr Tryjanowski, Michael A. Weston, Daniel T. Blumstein, and Tomáš Albrecht (2023). Bird tolerance to humans in open tropical ecosystems. Nature Communications, 14:2146. doi:10.1038/s41467-023-37936-5.

  10. Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen, Arno Solin, and Aki Vehtari (2023). Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Statistics and Computing, 33(17):1-28. doi:10.1007/s11222-022-10167-2. arXiv preprint arXiv:2004.11408.

Pre-prints

  1. Lauren Kennedy, Aki Vehtari, and Andrew Gelman (2023). Scoring multilevel regression and poststratification based population and subpopulation estimates. arXiv preprint arXiv:2312.06334.

  2. Alex Cooper, Aki Vehtari, Catherine Forbes, Lauren Kennedy, and Dan Simpson (2023). Bayesian cross-validation by parallel Markov chain Monte Carlo. arXiv preprint arXiv:2310.07002.

  3. Yann McLatchie and Aki Vehtari (2023). Efficient estimation and correction of selection-induced bias with order statistics. arXiv preprint arXiv:2309.03742.

  4. Yann McLatchie, Sölvi Rögnvaldsson, Frank Weber, and Aki Vehtari (2023). Robust and efficient projection predictive inference. arXiv preprint arXiv:2306.15581.

  5. Frank Weber, Änne Glass, and Aki Vehtari (2023). Projection predictive variable selection for discrete response families with finite support. arXiv preprint arXiv:2301.01660.

jd asked Andrew “which paper from 2023 do you like best?”, and I also find it difficult to choose one. I highlight two papers, but I’m proud of all of them!

“Detecting and diagnosing prior and likelihood sensitivity with power-scaling” is based on an idea that had been on my todo list for a very long time, and seeing that it works so well and can have practical software implementation was really nice.

In “Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming” we didn’t come up with a new GP approximation, but we were able to develop simple diagnostics to tell whether we have enough basis functions. I just love when diagnostics can answer frequently asked questions like “How do I choose the number of basis functions?”