This repo is the code for the 2024 IEEE PES GM paper. It proposes a novel topology embedding method for handling topology problem in power system.

Stochastic Process Library for Python

Gaussian process explorations

Minimal Implementation of Bayesian Optimization in JAX

A Neural Network that predicts the direction of a force experienced by the Syntouch Biotac robotic finger

Surrogate Final BH properties

Project for the Data Science PhD course of Probability

Recursive Gaussian Process regression allows performing GP regression, while also being able to add train the model at runtime

MSc Dissertation: Estimating Uncertainty in Machine Learning Models for Drug Discovery

Code for "Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties"

Practical session on implementing probabilistic linear solvers at the Probabilistic Numerics Spring School 2024

Hierarchical Gaussian Processes based Multi-Robot Relative Localization

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

Rust implementation of Gaussian Process regression

Engineering Thesis written to predict fault detection in wind turbines using Python

This is the recent work of my on the importance and application of mathematical function around its Hilbert function theory on artificial intelligence algorithms. The main motivation was the desire of improving the convergence rate and learning rate of various learning algorithms via Generalized Gaussian Radial Basis Function.

Gaussian Process Regression vs. Relevance Vector Machine.

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

A multi-target regression algorithm based on Gaussian process regression

Learning-aided 3D mapping