AvGPR is a package that calculates a weighted average Gaussian Process regression model over 5 implementations from packages in both R and Python.
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Updated
Feb 23, 2023 - R
AvGPR is a package that calculates a weighted average Gaussian Process regression model over 5 implementations from packages in both R and Python.
Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression
Machinelearning_algorithms_scratch
An implementation of 4 machine learning algorithms from scratch
Available R-Packages for Gaussian Process Regression
This repository contains Jupyter Notebook file containing the code to compare different sklearn classifiers on a dataset. Then it saves the output .png results in the working folder.
GAP-AL tutorial for the Psi-k ML-IP 2021 tutorial workshop.
Gaussian Processes for Machine Learning
Prediction of the net hourly generated energy of a Combined Cycle Power Plant using Gaussian Process Regression (GPR).
卒業研究の実験のために書いたソースコードです。全てのコードを1から書きました。(自動生成されたコードであるcython_wl_kernel.cppを除く)
Model for 2019 March Madness, 37th place on Kaggle + Google Cloud & NCAA ML Competition 2019
A C++ implementation of Deep Gaussian Process with Stochastic Imputation
A Neural Network that predicts the direction of a force experienced by the Syntouch Biotac robotic finger
Project for the Data Science PhD course of Probability
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.
A multi-target regression algorithm based on Gaussian process regression
Construct a non-parametric framework based on Gaussian process regression to infer gravitational potential from a stellar kinematical snapshot
For my master's thesis, I investigate the predictive power of several linear and non-linear approaches for crude oil prices, among which linear time series models, neural networks, Gaussian process regression, and time-varying coefficient models.
Practical session on implementing probabilistic linear solvers at the Probabilistic Numerics Spring School 2024
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