Bayesflow: Amortized bayesian workflows with neural networks
Modern Bayesian inference involves a mixture of computational techniques for estimating,
validating, and drawing conclusions from probabilistic models as part of principled …
validating, and drawing conclusions from probabilistic models as part of principled …
Neural superstatistics for Bayesian estimation of dynamic cognitive models
Mathematical models of cognition are often memoryless and ignore potential fluctuations of
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
A deep learning method for comparing Bayesian hierarchical models.
Bayesian model comparison (BMC) offers a principled approach to assessing the relative
merits of competing computational models and propagating uncertainty into model selection …
merits of competing computational models and propagating uncertainty into model selection …
Misspecification-robust sequential neural likelihood for simulation-based inference
Simulation-based inference techniques are indispensable for parameter estimation of
mechanistic and simulable models with intractable likelihoods. While traditional statistical …
mechanistic and simulable models with intractable likelihoods. While traditional statistical …
Efficient estimation and correction of selection-induced bias with order statistics
Y McLatchie, A Vehtari - Statistics and Computing, 2024 - Springer
Abstract Model selection aims to identify a sufficiently well performing model that is possibly
simpler than the most complex model among a pool of candidates. However, the decision …
simpler than the most complex model among a pool of candidates. However, the decision …
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Mathematical models of cognition are often memoryless and ignore potential fluctuations of
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas
The simplex projection expands the capabilities of simplex plots (also known as ternary
plots) to achieve a lossless visualization of 4D compositional data on a 2D canvas …
plots) to achieve a lossless visualization of 4D compositional data on a 2D canvas …
[HTML][HTML] Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations
Foraging theory has been a remarkably successful approach to understanding the behavior
of animals in many contexts. In patch-based foraging contexts, the marginal value theorem …
of animals in many contexts. In patch-based foraging contexts, the marginal value theorem …
Automating Model Comparison in Factor Graphs
Bayesian state and parameter estimation are automated effectively in a variety of
probabilistic programming languages. The process of model comparison on the other hand …
probabilistic programming languages. The process of model comparison on the other hand …
Structural link prediction model with multi-view text semantic feature extraction
K Chen, T Zhang, Y Zhao… - Intelligent Decision …, 2024 - journals.sagepub.com
The exponential expansion of information has made text feature extraction based on simple
semantic information insufficient for the multidimensional recognition of textual data. In this …
semantic information insufficient for the multidimensional recognition of textual data. In this …