Bayesian Computation using Design of Experiments-based Interpolation Technique in R
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Updated
Dec 3, 2018 - R
Bayesian Computation using Design of Experiments-based Interpolation Technique in R
bayesian bootstrapping in python
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.
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.
Code for "A New Look at TFPI Inhibition of Factor X Activation"
Approximate variational inference in Julia
Applying amortizing neural posterior estimation for non-linear mixed effects models
Langevin Gradient Parallel Tempering for Bayesian Neural Learning.
Official implementation of "Sample Size Determination: Posterior Distributions Proximity"
Metric Gaussian Variational Inference
👻 Utilities for analyzing Bayesian models and posterior distributions
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