39 citations · 159 across the 32 of their papers we have counts for
5 papers · 1 filter
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…
Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models
Florence Bockting, Stefan T. Radev, Paul-Christian Bürkner
A central characteristic of Bayesian statistics is the ability to consistently incorporate prior knowledge into various modeling processes. In this paper, we focus on translating d…
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Lukas Schumacher, Paul-Christian Bürkner, Andreas Voss +2
Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to aug…
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Paul-Christian Bürkner, Maximilian Scholz, Stefan T. Radev
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of s…
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…