Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures. - Tan Zhou; Sorin C. Popescu; A. Michelle Lawing; Marian Eriksson; Bogdan M. Strimbu; Paul C. Bürkner (Remote. Sens., 2018)

Advanced Bayesian Multilevel Modeling with the R Package brms. - Paul-Christian Bürkner (R J., 2018)

thurstonianIRT: Thurstonian IRT Models in R. - Paul-Christian Bürkner (J. Open Source Softw., 2019)

Flexible Prior Elicitation via the Prior Predictive Distribution. - Marcelo Hartmann; Georgi Agiashvili; Paul C. Bürkner; Arto Klami (UAI, 2020)

Amortized Bayesian Inference for Models of Cognition. - Stefan T. Radev; Andreas Voss; Eva Marie Wieschen; Paul-Christian Bürkner (CoRR, 2020)

Impaired context-sensitive adjustment of behaviour in Parkinson's disease patients tested on and off medication: An fMRI study. - Ima Trempler; Paul-Christian Bürkner; Nadiya El-Sourani; Ellen Binder; Paul Reker; Gereon R. Fink; Ricarda I. Schubotz (NeuroImage, 2020)

BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. - Marvin Schmitt; Paul-Christian Bürkner; Ullrich Köthe; Stefan T. Radev (CoRR, 2021)

Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models. - Paul-Christian Bürkner; Jonah Gabry; Aki Vehtari (Comput. Stat., 2021)

Bayesian Item Response Modeling in R with brms and Stan. - Paul-Christian Bürkner (J. Stat. Softw., 2021)

Poisson regression for linguists: A tutorial introduction to modelling count data with brms. - Bodo Winter; Paul-Christian Bürkner (Lang. Linguistics Compass, 2021)

Implicitly adaptive importance sampling. - Topi Paananen; Juho Piironen; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2021)

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. - Alejandro Catalina; Paul-Christian Bürkner; Aki Vehtari (AISTATS, 2022)

Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort. - Tuulia Malén; Tomi Karjalainen; Janne Isojärvi; Aki Vehtari; Paul-Christian Bürkner; Vesa Putkinen; Valtteri Kaasinen; Jarmo Hietala; Pirjo Nuutila; Juha O. Rinne; Lauri Nummenmaa (NeuroImage, 2022)

Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. - Teemu Säilynoja; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2022)

Meta-Uncertainty in Bayesian Model Comparison. - Marvin Schmitt; Stefan T. Radev; Paul-Christian Bürkner (AISTATS, 2023)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks. - Marvin Schmitt; Paul-Christian Bürkner; Ullrich Köthe; Stefan T. Radev (DAGM, 2023)

Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. - Lorenzo Perini; Paul-Christian Bürkner; Arto Klami (ICML, 2023)

Jana: Jointly amortized neural approximation of complex Bayesian models. - Stefan T. Radev; Marvin Schmitt; Valentin Pratz; Umberto Picchini; Ullrich Köthe; Paul-Christian Bürkner (UAI, 2023)

A Deep Learning Method for Comparing Bayesian Hierarchical Models. - Lasse Elsemüller; Martin Schnuerch; Paul-Christian Bürkner; Stefan T. Radev (CoRR, 2023)

Inferring Human Intentions from Predicted Action Probabilities. - Lei Shi 032; Paul-Christian Bürkner; Andreas Bulling (CoRR, 2023)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. - Marvin Schmitt; Daniel Habermann; Paul-Christian Bürkner; Ullrich Köthe; Stefan T. Radev (CoRR, 2023)

Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference. - Marvin Schmitt; Stefan T. Radev; Paul-Christian Bürkner (CoRR, 2023)

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. - Gabriel Riutort-Mayol; Paul-Christian Bürkner; Michael Riis Andersen; Arno Solin; Aki Vehtari (Stat. Comput., 2023)

Using reference models in variable selection. - Federico Pavone; Juho Piironen; Paul-Christian Bürkner; Aki Vehtari (Comput. Stat., 2023)

Amortized Bayesian Model Comparison With Evidential Deep Learning. - Stefan T. Radev; Marco D'Alessandro; Ulf K. Mertens; Andreas Voss; Ullrich Köthe; Paul-Christian Bürkner (IEEE Trans. Neural Networks Learn. Syst., 2023)

A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms. - Paul-Christian Bürkner; Ilja Kröker; Sergey Oladyshkin; Wolfgang Nowak (J. Comput. Phys., 2023)

BayesFlow: Amortized Bayesian Workflows With Neural Networks. - Stefan T. Radev; Marvin Schmitt; Lukas Schumacher; Lasse Elsemüller; Valentin Pratz; Yannik Schälte; Ullrich Köthe; Paul-Christian Bürkner (J. Open Source Softw., 2023)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. - Marvin Schmitt; Desi R. Ivanova; Daniel Habermann; Ullrich Köthe; Paul-Christian Bürkner; Stefan T. Radev (ICML, 2024)

Consistency Models for Scalable and Fast Simulation-Based Inference. - Marvin Schmitt; Valentin Pratz; Ullrich Köthe; Paul-Christian Bürkner; Stefan T. Radev (NeurIPS, 2024)

The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas. - Marvin Schmitt; Yuga Hikida; Stefan T. Radev; Filip Sadlo; Paul-Christian Bürkner (CoRR, 2024)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation. - Marvin Schmitt; Paul-Christian Bürkner; Ullrich Köthe; Stefan T. Radev (CoRR, 2024)

Amortized Bayesian Multilevel Models. - Daniel Habermann; Marvin Schmitt; Lars Kühmichel; Andreas Bulling; Stefan T. Radev; Paul-Christian Bürkner (CoRR, 2024)

Amortized Bayesian Workflow (Extended Abstract). - Marvin Schmitt; Chengkun Li; Aki Vehtari; Luigi Acerbi; Paul-Christian Bürkner; Stefan T. Radev (CoRR, 2024)

Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy. - Philipp Reiser; Paul-Christian Bürkner; Anneli Guthke (CoRR, 2024)

Sensitivity-Aware Amortized Bayesian Inference. - Lasse Elsemüller; Hans Olischläger; Marvin Schmitt; Paul-Christian Bürkner; Ullrich Köthe; Stefan T. Radev (Trans. Mach. Learn. Res., 2024)

Detecting and diagnosing prior and likelihood sensitivity with power-scaling. - Noa Kallioinen; Topi Paananen; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2024)

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. - Måns Magnusson; Jakob Torgander; Paul-Christian Bürkner; Lu Zhang; Bob Carpenter; Aki Vehtari (AISTATS, 2025)

ActionDiffusion: An Action-Aware Diffusion Model for Procedure Planning in Instructional Videos. - Lei Shi 032; Paul C. Bürkner; Andreas Bulling (WACV, 2025)

Amortized Bayesian Mixture Models. - Simon Kucharský; Paul-Christian Bürkner (CoRR, 2025)

Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data. - Aayush Mishra; Daniel Habermann; Marvin Schmitt; Stefan T. Radev; Paul-Christian Bürkner (CoRR, 2025)

Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation. - Lasse Elsemüller; Valentin Pratz; Mischa von Krause; Andreas Voss; Paul-Christian Bürkner; Stefan T. Radev (CoRR, 2025)

Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models. - Stefania Scheurer; Philipp Reiser; Tim Brünnette; Wolfgang Nowak; Anneli Guthke; Paul-Christian Bürkner (CoRR, 2025)

Uncertainty quantification and propagation in surrogate-based Bayesian inference. - Philipp Reiser; Javier Enrique Aguilar; Anneli Guthke; Paul-Christian Bürkner (Stat. Comput., 2025)

Expert-elicitation method for non-parametric joint priors using normalizing flows. - Florence Bockting; Stefan T. Radev; Paul-Christian Bürkner (Stat. Comput., 2025)

DGP-LVM: Derivative Gaussian process latent variable models. - Soham Mukherjee; Manfred Claassen; Paul-Christian Bürkner (Stat. Comput., 2025)