This tutorial is a reasonably self-contained tutorial and documentation source about\ntrasitioning to bayesian analysis from traditional first order estimation techniques\nfor nonlinear-mixed-effects ...

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical ...

0_data_generation.ipynb [Hidden] Data generation ⚠️ Not for initial use. This notebook simulates the kinetic dataset. Use it only if you want to inspect or regenerate the synthetic data.

This tutorial aims to provide clinical researchers working in drug or device development with an introduction to key Bayesian concepts. We assume the reader has a basic understanding of more ...

Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo ...

Approximate Bayesian computation (ABC) algorithms are a class of Monte Carlo methods for doing inference when the likelihood function can be simulated from, but not explicitly evaluated. This ...

ABSTRACT: An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is ...

ABSTRACT: Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for ...