Different components of cognitive-behavioral therapy affect specific cognitive mechanisms
Psychological therapies are among the most effective treatments for common mental health
problems—however, we still know relatively little about how exactly they improve symptoms …
problems—however, we still know relatively little about how exactly they improve symptoms …
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 applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …
quantitative sciences and industrial areas. This development is driven by a combination of …
Robust and efficient projection predictive inference
Y McLatchie, S Rögnvaldsson, F Weber… - arXiv preprint arXiv …, 2023 - arxiv.org
The concepts of Bayesian prediction, model comparison, and model selection have
developed significantly over the last decade. As a result, the Bayesian community has …
developed significantly over the last decade. As a result, the Bayesian community has …
A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances
Ecological disturbances are becoming more extensive and intensive globally, a trend
exemplified by 'megafires' and industrial deforestation, which cause widespread losses of …
exemplified by 'megafires' and industrial deforestation, which cause widespread losses of …
Opaque prior distributions in Bayesian latent variable models
We review common situations in Bayesian latent variable models where the prior distribution
that a researcher specifies differs from the prior distribution used during estimation. These …
that a researcher specifies differs from the prior distribution used during estimation. These …
Gaussian distributional structural equation models: A framework for modeling latent heteroscedasticity
L Fazio, PC Bürkner - arXiv preprint arXiv:2404.14124, 2024 - arxiv.org
Accounting for the complexity of psychological theories requires methods that can predict
not only changes in the means of latent variables--such as personality factors, creativity, or …
not only changes in the means of latent variables--such as personality factors, creativity, or …
The tenets of quantile-based inference in Bayesian models
Bayesian inference can be extended to probability distributions defined in terms of their
inverse distribution function, ie their quantile function. This applies to both prior and …
inverse distribution function, ie their quantile function. This applies to both prior and …
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Surrogate models are statistical or conceptual approximations for more complex simulation
models. In this context, it is crucial to propagate the uncertainty induced by limited simulation …
models. In this context, it is crucial to propagate the uncertainty induced by limited simulation …
The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses
F Henrich, R Hartmann, V Pratz, A Voss… - Behavior Research …, 2024 - Springer
Diffusion models have been widely used to obtain information about cognitive processes
from the analysis of responses and response-time data in two-alternative forced-choice …
from the analysis of responses and response-time data in two-alternative forced-choice …
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
M Magnusson, J Torgander, PC Bürkner… - arXiv preprint arXiv …, 2024 - arxiv.org
The generality and robustness of inference algorithms is critical to the success of widely
used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing. jl. When …
used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing. jl. When …