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08/23/2023
Inferring Human Intentions from Predicted Action Probabilities
Predicting the next action that a human is most likely to perform is key...
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08/22/2023
Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models
A central characteristic of Bayesian statistics is the ability to consis...
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06/28/2023
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques...
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02/17/2023
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ”jointly amortized neural approximation” (JANA) of in...
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01/27/2023
A Deep Learning Method for Comparing Bayesian Hierarchical Models
Bayesian model comparison (BMC) offers a principled approach for assessi...
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11/23/2022
Neural Superstatistics: A Bayesian Method for Estimating Dynamic Models of Cognition
Mathematical models of cognition are often memoryless and ignore potenti...
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11/22/2022
Optimal design of the Wilcoxon-Mann-Whitney-test
In scientific research, many hypotheses relate to the comparison of two ...
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11/22/2022
Testing for Publication Bias in Diagnostic Meta-Analysis: A Simulation Study
The present study investigates the performance of several statistical te...
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10/13/2022
Meta-Uncertainty in Bayesian Model Comparison
Bayesian model comparison (BMC) offers a principled probabilistic approa...
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10/13/2022
Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models
Bayesian modeling provides a principled approach to quantifying uncertai...
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09/06/2022
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Probabilistic (Bayesian) modeling has experienced a surge of application...
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08/15/2022
Intuitive Joint Priors for Bayesian Linear Multilevel Models: The R2D2M2 prior
The training of high-dimensional regression models on comparably sparse ...
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04/12/2022
The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coefficients and global selection of terms
Polynomial chaos expansion (PCE) is a versatile tool widely used in unce...
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12/16/2021
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference
Neural density estimators have proven remarkably powerful in performing ...
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12/01/2021
Prior knowledge elicitation: The past, present, and future
Specification of the prior distribution for a Bayesian model is a centra...
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07/29/2021
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Determining the sensitivity of the posterior to perturbations of the pri...
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03/18/2021
Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison
Assessing goodness of fit to a given distribution plays an important rol...
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03/15/2021
Workflow Techniques for the Robust Use of Bayes Factors
Inferences about hypotheses are ubiquitous in the cognitive sciences. Ba...
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11/03/2020
Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
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10/14/2020
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Projection predictive inference is a decision theoretic Bayesian approac...
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05/08/2020
Amortized Bayesian Inference for Models of Cognition
As models of cognition grow in complexity and number of parameters, Baye...
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05/06/2020
Group Heterogeneity Assessment for Multilevel Models
Many data sets contain an inherent multilevel structure, for example, be...
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04/27/2020
Using reference models in variable selection
Variable selection, or more generally, model reduction is an important a...
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04/23/2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful non-parametric probabilistic models for ...
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04/22/2020
Amortized Bayesian model comparison with evidential deep learning
Comparing competing mathematical models of complex natural processes is ...
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06/20/2019
Pushing the Limits of Importance Sampling through Iterative Moment Matching
The accuracy of an integral approximation via Monte Carlo sampling depen...
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05/23/2019
Bayesian Item Response Modelling in R with brms and Stan
Item Response Theory (IRT) is widely applied in the human sciences to mo...
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03/19/2019
Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC
Markov chain Monte Carlo is a key computational tool in Bayesian statist...
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02/17/2019
Approximate leave-future-out cross-validation for Bayesian time series models
One of the common goals of time series analysis is to use the observed s...
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02/17/2019
Approximate leave-future-out cross-validation for time series models
One of the common goals of time series analysis is to use the observed s...
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10/24/2018
Leave-one-out cross-validation for non-factorizable normal models
Cross-validation can be used to measure a model's predictive accuracy fo...
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03/17/2018