
Prior knowledge elicitation: The past, present, and future
Specification of the prior distribution for a Bayesian model is a centra...
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Detecting and diagnosing prior and likelihood sensitivity with powerscaling
Determining the sensitivity of the posterior to perturbations of the pri...
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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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Workflow Techniques for the Robust Use of Bayes Factors
Inferences about hypotheses are ubiquitous in the cognitive sciences. Ba...
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Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
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Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Projection predictive inference is a decision theoretic Bayesian approac...
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Amortized Bayesian Inference for Models of Cognition
As models of cognition grow in complexity and number of parameters, Baye...
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Group Heterogeneity Assessment for Multilevel Models
Many data sets contain an inherent multilevel structure, for example, be...
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Using reference models in variable selection
Variable selection, or more generally, model reduction is an important a...
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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful nonparametric probabilistic models for ...
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Amortized Bayesian model comparison with evidential deep learning
Comparing competing mathematical models of complex natural processes is ...
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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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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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Ranknormalization, 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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Approximate leavefutureout crossvalidation for Bayesian time series models
One of the common goals of time series analysis is to use the observed s...
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Approximate leavefutureout crossvalidation for time series models
One of the common goals of time series analysis is to use the observed s...
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Leaveoneout crossvalidation for nonfactorizable normal models
Crossvalidation can be used to measure a model's predictive accuracy fo...
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Optimal Designs for the Generalized Partial Credit Model
Analyzing ordinal data becomes increasingly important in psychology, esp...
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PaulChristian Bürkner
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