Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England

eth-cscs/abcpy 29 Jun 2020

A mathematical model for the COVID-19 pandemic spread in England is presented.

Applications Physics and Society Populations and Evolution

108
0.04 stars / hour

Bayesian Workflow

storopoli/Bayesian-Julia 3 Nov 2020

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory.

Methodology

152
0.03 stars / hour

PyDMD: A Python package for robust dynamic mode decomposition

pydmd/pydmd 12 Feb 2024

The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis.

Computation Systems and Control Systems and Control Dynamical Systems Computational Physics

749
0.03 stars / hour

Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models

blent-ai/ALEPython 27 Dec 2016

When fitting black box supervised learning models (e. g., complex trees, neural networks, boosted trees, random forests, nearest neighbors, local kernel-weighted methods, etc.

Methodology

143
0.02 stars / hour

Introducing libeemd: A program package for performing the ensemble empirical mode decomposition

helske/Rlibeemd 3 Jul 2017

The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD).

Computation

36
0.02 stars / hour

Functional Data Analysis: An Introduction and Recent Developments

davidruegamer/fda_tutorial 9 Dec 2023

The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry, and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data.

Methodology Applications

6
0.02 stars / hour

ruptures: change point detection in Python

deepcharles/ruptures 2 Jan 2018

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

1,454
0.02 stars / hour

Estimating Fold Changes from Partially Observed Outcomes with Applications in Microbial Metagenomics

statdivlab/rademu 7 Feb 2024

We consider the problem of estimating fold-changes in the expected value of a multivariate outcome that is observed subject to unknown sample-specific and category-specific perturbations.

Methodology Applications

13
0.01 stars / hour

Review of multi-fidelity models

mgisellef/reviewofmultifidelitymodels_toyproblems 23 Sep 2016

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources.

Applications 65C99, 65D15, 68W25, 76-00, 74-00 A.1; G.3; I.6.5; I.2.8

2
0.01 stars / hour

Predicting outcomes for games of skill by redefining what it means to win

morelandjs/melo 1 Feb 2018

The Elo rating system is a highly successful ranking algorithm for games of skill where, by construction, one team wins and the other loses.

Methodology

15
0.01 stars / hour