👻 Utilities for analyzing Bayesian models and posterior distributions
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Updated
Aug 22, 2024 - R
👻 Utilities for analyzing Bayesian models and posterior distributions
bayesian bootstrapping in python
We derive a fundamental property of the posterior distribution in Gaussian denoising, and use it to propose a new way for uncertainty visualization, which requires no training or fine-tuning.
Metric Gaussian Variational Inference
Applying amortizing neural posterior estimation for non-linear mixed effects models
Approximate variational inference in Julia
Langevin Gradient Parallel Tempering for Bayesian Neural Learning.
Bayesian Computation using Design of Experiments-based Interpolation Technique in R
Bayesian Inference on the risk factors for cervical cancer
Official implementation of "Sample Size Determination: Posterior Distributions Proximity"
A python module aimed at expediting the analysis of biological systems with ODE models
This incomplete repository is used to facilitate the consultation of individual files in this project. Only files smaller than 100 MB are available here. The complete project is available at http://doi.org/10.17605/OSF.IO/UERYQ.
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