👻 Utilities for analyzing Bayesian models and posterior distributions
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Updated
Oct 9, 2024 - R
👻 Utilities for analyzing Bayesian models and posterior distributions
Metric Gaussian Variational Inference
Official implementation of "Sample Size Determination: Posterior Distributions Proximity"
Langevin Gradient Parallel Tempering for Bayesian Neural Learning.
Applying amortizing neural posterior estimation for non-linear mixed effects models
Approximate variational inference in Julia
Code for "A New Look at TFPI Inhibition of Factor X Activation"
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.
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.
A python module aimed at expediting the analysis of biological systems with ODE models
Bayesian Inference on the risk factors for cervical cancer
bayesian bootstrapping in python
Bayesian Computation using Design of Experiments-based Interpolation Technique in R
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