Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
Welcome to the Simulation-Based Inference (SBI) Hackathon in Grenoble — a three-day, hands-on event dedicated to exploring modern Bayesian inference tools for complex scientific simulators. 👥 Target ...
In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a ...
Key Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources, Yangtze River Scientific Research Institute, Wuhan, China. Soil-water characteristic curve (SWCC) is ...
The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...
Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of ...
Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate ...
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results