12 citations · 39 across the 16 of their papers we have counts for
17 papers
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.…
Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques
Marvin Schmitt, Anne Schwerk, Sebastian Lempert
This study investigates the use of prompt engineering to enhance large language models (LLMs), specifically GPT-4o-mini and gemini-1.5-flash, in sentiment analysis tasks. It evalua…
Posterior SBC: Simulation-Based Calibration Checking Conditional on Data
Teemu Säilynoja, Marvin Schmitt, Paul-Christian Bürkner +1
Simulation-based calibration checking (SBC) refers to the validation of an inference algorithm and model implementation through repeated inference on data simulated from a generati…
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…
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…
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…
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…
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Stefan T Radev, Marvin Schmitt, Lukas Schumacher +5
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflo…
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
Stefan T. Radev, Marvin Schmitt, Valentin Pratz +3
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulati…
Meta-Uncertainty in Bayesian Model Comparison
Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner
Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models. In standard BMC, we construct a discrete probability distribution ove…
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe +1
Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow fram…