Home Statistics and Visualization Statistical Modeling, Causal Inference, and Social Science Progress in 2024 (Aki)

Progress in 2024 (Aki)

Statistical Modeling, Causal Inference, and Social Science 2025-01-07

Here’s my 2024 progress report. There are 5 publications common with Andrew in 2024.

Active Statistics book is the biggest in size, but personally getting the Pareto smoothed importance sampling paper published after 9 years from the first submission was a big event, too. I think I only blogged 2023 progress report and job ads (I sometimes have blog post ideas, but as I’m a slow writer, it’s difficult to find time to turn them to actual posts). I’m very happy with the progress in 2024, but also excited on what we are going to get done in 2025!

Book

Papers published or accepted for publication in 2024

  • Yann McLatchie, Sölvi Rögnvaldsson, Frank Weber, and Aki Vehtari (2025). Advances in projection predictive inference. Statistical Science, accepted for publication. arXiv preprint arXiv:2306.15581. Software: projpred, kulprit.

  • Christopher Tosh, Philip Greengard, Ben Goodrich, Andrew Gelman, Aki Vehtari, and Daniel Hsu (2025). The piranha problem: Large effects swimming in a small pond. Notices of the American Mathematical Society, 72(1):15-25. arXiv preprint arXiv:2105.13445.

  • Kunal Ghosh, Milica Todorović, Aki Vehtari, and Patrick Rinke (2025). Active learning of molecular data for task-specific objectives. The Journal of Chemical Physics, doi:10.1063/5.0229834.

  • Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov, Lionel Riou-Durand, Aki Vehtari, and Andrew Gelman (2024). Nested Rhat: Assessing the convergence of Markov chain Monte Carlo when running many short chains. Bayesian Analysis, doi:10.1214/24-BA1453.Software: posterior.

  • Yann McLatchie and Aki Vehtari (2024). Efficient estimation and correction of selection-induced bias with order statistics. Statistics and Computing, 34(132). doi:10.1007/s11222-024-10442-4.

  • Frank Weber, Änne Glass, and Aki Vehtari (2024). Projection predictive variable selection for discrete response families with finite support. Computational Statistics, doi:10.1007/s00180-024-01506-0. Software projpred.

  • Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58. Online. Software: loo, posterior, ArviZ

  • Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, and Jonathan H. Huggins (2024). A framework for improving the reliability of black-box variational inference. Journal of Machine Learning Research, 25(219):1-71. Online.

  • Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, and Aki Vehtari (2024). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Statistics and Computing, 34(57). Online. Supplementary code. Software: priorsense

  • Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, and Aki Vehtari (2024). Past, present, and future of software for Bayesian inference. Statistical Science, 39(1):46-61. Online.

  • Alex Cooper, Dan Simpson, Lauren Kennedy, Catherine Forbes, and Aki Vehtari (2024). Cross-validatory model selection for Bayesian autoregressions with exogenous regressors. Bayesian Analysis, doi:10.1214/23-BA1409.

  • Marta Kołczyńska, Paul-Christian Bürkner, Lauren Kennedy, and Aki Vehtari (2024). Trust in state institutions in Europe, 1989–2019. Survey Research Methods, 18(1). doi:10.18148/srm/2024.v18i1.8119.

  • Alex Cooper, Aki Vehtari, Catherine Forbes, Lauren Kennedy, and Dan Simpson (2024). Bayesian cross-validation by parallel Markov chain Monte Carlo. Statistics and Computing, 34:119. doi:10.1007/s11222-024-10404-w.

  • Ryoko Noda, Michael Francis Mechenich, Juha Saarinen, Aki Vehtari, Indrė Žliobaitė (2024). Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. Ecological Informatics, 82:102658. doi:10.1016/j.ecoinf.2024.102658.

  • 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 (2024). Prior knowledge elicitation: The past, present, and future. Bayesian Analysis, 19(49):1129-1161. doi:10.1214/23-BA1381.

arXived in 2024

  • Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, and Stefan T. Radev (2024). Amortized Bayesian Workflow (Extended Abstract). arXiv preprint arXiv:2409.04332.

  • Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner, Lu Zhang, Bob Carpenter, and Aki Vehtari (2024). posteriordb: Testing, benchmarking and developing Bayesian inference algorithms. arXiv preprint arXiv:2407.04967. Database and software: posteriordb

  • David Kohns, Noa Kallionen, Yann McLatchie, and Aki Vehtari (2024). The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions. arXiv preprint arXiv:2405.19920.

  • Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta, and Aki Vehtari (2024). Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis. arXiv preprint arXiv:2404.01688.

  • Guangzhao Cheng, Aki Vehtari, and Lu Cheng (2024). Raw signal segmentation for estimating RNA modifications and structures from Nanopore direct RNA sequencing data. bioRxiv preprint.

Software

Case studies

  • Aki Vehtari (2024). Nabiximols. Model checking and comparison, comparison of continuous and discrete models, LOO-PIT checking, calibration plots, prior sensitivity analysis, model refinement, treatment effect, effect of model mis-specification.

  • Aki Vehtari (2024). Birthdays. Workflow example for iterative building of a time series model. In 2024, added demonstration of Pathfinder for quick initial results and MCMC initialization.

FAQ

Video