39 citations · 159 across the 32 of their papers we have counts for
39 papers
Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference
Jonas Arruda, Jan Hasenauer, Stefan T. Radev
Flow matching has emerged as a powerful generative family for simulation-based inference (SBI). However, sampling and density evaluation require numerical integration at inference…
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…
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.…
CogFormer: Learn All Your Models Once
Jerry M. Huang, Lukas Schumacher, Niek Stevenson +1
Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fit complex models that were previously…
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,…
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…
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…
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
Yufei Wu, Stefan T. Radev, Francis Tuerlinckx
Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In thi…
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…
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…
Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
Lukas Schumacher, Martin Schnuerch, Andreas Voss +1
Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such…
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
Jens Müller, Lars Kühmichel, Martin Rohbeck +2
In this work, we analyze the conditions under which information about the context of an input can improve the predictions of deep learning models in new domains. Following work…
Consistency Models for Scalable and Fast Simulation-Based Inference
Marvin Schmitt, Valentin Pratz, Ullrich Köthe +2
Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with t…
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference
Marvin Schmitt, Leona Odole, Stefan T. Radev +1
We present multimodal neural posterior estimation (MultiNPE), a method to integrate heterogeneous data from different sources in simulation-based inference with neural networks. In…
Sensitivity-Aware Amortized Bayesian Inference
Lasse Elsemüller, Hans Olischläger, Marvin Schmitt +3
Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient fo…
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
Marvin Schmitt, Desi R. Ivanova, Daniel Habermann +3
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and dat…