52 citations · 167 across the 19 of their papers we have counts for
24 papers
Approximating Bayesian leave-one-group-out cross-validation
Anna Elisabeth Riha, Svenja Jedhoff, Paul-Christian Bürkner +1
When data are grouped, hierarchical or multilevel models are commonly used to account for group-level variation with group-specific parameters. Leave-one-group-out cross-validation…
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3
We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointl…
Simulation-based validation of Bayes factor computation
Martin Modrák, Sebastian Stroppel, Paul-Christian Bürkner
We propose and evaluate two methods that validate the computation of Bayes factors: one based on an improved variant of simulation-based calibration checking (SBC) and one based on…
Efficient Uncertainty Propagation in Bayesian Two-Step Procedures
Svenja Jedhoff, Hadi Kutabi, Anne Meyer +1
Bayesian inference provides a principled framework for probabilistic reasoning. If inference is performed in two steps, uncertainty propagation plays a crucial role in accounting f…
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Stefania Scheurer, Philipp Reiser, Tim Brünnette +3
Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortiz…
Amortized Bayesian Mixture Models
Šimon Kucharský, Paul Christian Bürkner
Finite mixtures are a broad class of models useful in scenarios where observed data is generated by multiple distinct processes but without explicit information about the responsib…