1.7k citations · 2.2k across the 46 of their papers we have counts for
52 papers
Proximal R2 Priors for Structured Variance Allocation
Javier Enrique Aguilar, David Kohns, Eric Yanchenko +4
Scientific domain knowledge in regression often concerns structural relationships among model terms, which are typically encoded as parameter constraints. Since such relationships…
Semi-Amortized Bayesian Inference with Reusable Local Posteriors
Niek Stevenson, Paul-Christian Bürkner, Andrew Heathcote +1
Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable. However, learning the full joint posterior…
Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
Daniel Habermann, Andreas Bulling, Stefan T. Radev +1
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method…
Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
Hans Olischläger, Svenja Jedhoff, Šimon Kucharský +3
Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and of…
High-Dimensional Simulation-Based Inference in Latent Spaces
Lars Kühmichel, Stefan T. Radev, Bhanu Prasanna Koppolu +3
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations…
To select or not to select: predictively consistent priors instead of model selection
Anna Elisabeth Riha, Leevi Lindgren, David Kohns +2
Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complex…
Latent Variable Models for Distributional Features
Luna Fazio, Paul-Christian Bürkner
Analyzing the mean response of study subjects in psychological research is a standard, well-justified practice. However, theoretical arguments and empirical evidence also suggest t…
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However,…
Latent variable estimation with composite Hilbert space Gaussian processes
Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago +2
We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple da…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. However, the accuracy of neural surrogate…
R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari +1
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By dec…
elicito: A Python Package for Expert Prior Elicitation
Florence Bockting, Paul-Christian Bürkner
Expert prior elicitation plays a critical role in Bayesian analysis by enabling the specification of prior distributions that reflect domain knowledge. However, expert knowledge of…
Dependency-Aware Shrinkage Priors for High Dimensional Regression
Javier Enrique Aguilar, Paul-Christian Bürkner
In high dimensional regression, global local shrinkage priors have gained significant traction for their ability to yield sparse estimates, improve parameter recovery, and support…
Simulations in Statistical Workflows
Paul-Christian Bürkner, Marvin Schmitt, Stefan T. Radev
Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation, and even directly in model inference. Over the pas…
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
Lasse Elsemüller, Valentin Pratz, Mischa von Krause +3
Neural networks are fragile when confronted with data that significantly deviates from their training distribution. This is true in particular for simulation-based inference method…
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
Aayush Mishra, Daniel Habermann, Marvin Schmitt +2
Amortized Bayesian inference (ABI) with neural networks can solve probabilistic inverse problems orders of magnitude faster than classical methods. However, ABI is not yet sufficie…
Expert-elicitation method for non-parametric joint priors using normalizing flows
Florence Bockting, Stefan T. Radev, Paul-Christian Bürkner
We propose an expert-elicitation method for learning non-parametric joint prior distributions using normalizing flows. Normalizing flows are a class of generative models that enabl…
Amortized Bayesian Workflow
Chengkun Li, Aki Vehtari, Paul-Christian Bürkner +3
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-st…
Amortized Bayesian Multilevel Models
Daniel Habermann, Marvin Schmitt, Lars Kühmichel +3
Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully pro…
Primed Priors for Simulation-Based Validation of Bayesian Models
Luna Fazio, Maximilian Scholz, Javier Enrique Aguilar +1
Simulation-based calibration (SBC) is a method for validating inference algorithms and model implementations through repeated inference on data simulated from a generative model. F…
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe +1
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation pr…
The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas
Marvin Schmitt, Yuga Hikida, Stefan T Radev +2
The simplex projection expands the capabilities of simplex plots (also known as ternary plots) to achieve a lossless visualization of 4D compositional data on a 2D canvas. Previous…
Generalized Decomposition Priors on R2
Javier Enrique Aguilar, Paul-Christian Bürkner
The adoption of continuous shrinkage priors in high-dimensional linear models has gained widespread attention due to their practical and theoretical advantages. Among them, the R2D…