👻 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

Approximate variational inference in Julia

Langevin Gradient Parallel Tempering for Bayesian Neural Learning.

Bayesian Computation using Design of Experiments-based Interpolation Technique in R

Applying amortizing neural posterior estimation for non-linear mixed effects models

Bayesian Inference on the risk factors for cervical cancer

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.