👻 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

A Model and Bayesian approach to estimate kinetic parameters of TFPI inhibition of blood clotting factor X activation

Various codes centered around problems in Bayesian inference, Bayesian Linear Regression, and Bayesian Non-Linear Parameter Estimation using the application of various Markov Chain Monte Carlo Algorithms

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.