Efficient approximate Bayesian machine learning
-
Updated
Jun 4, 2024 - Python
Efficient approximate Bayesian machine learning
A software package for flexible HPC GPs
Geostatistical processes for the GeoStats.jl framework
A simple tool to help you with Gaussian calculations
Non-parametric density inference for single-cell analysis.
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods
Gaussian Process Model Building Interface
Gaussian processes in TensorFlow
Combining tree-boosting with Gaussian process and mixed effects models
Plots of Gaussian processes with AbstractGPs and Makie
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Quantile set inversion [arXiv:2211.01008] — Numerical experiments
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
This Python program demonstrates applying Gaussian blurring with different kernel sizes and sigma values to an image using OpenCV.
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
Deterministic algorithms for objective Bayesian inference and hyperparameter optimization
Using Linear Algebra Techniques to accelerate Gaussian Process Regression
Repo for My Master thesis "Portfolio Optimization with Gaussian Process" @tum Scientific Computing Chair, under guidance of @KislayaRavi
multi-fidelity fusion toolbox with (MF) Bayesian optimization
Scalable 1D Gaussian Processes in C++, Python, and Julia
Add a description, image, and links to the gaussian-processes topic page so that developers can more easily learn about it.
To associate your repository with the gaussian-processes topic, visit your repo's landing page and select "manage topics."