LinkedIn

"A Practical Guide to Estimating Probability of Default Using Bayesian Statistics in Python"

Credit risk modelling is a cornerstone of modern finance, enabling lenders to quantify the risk that a borrower will default on their obligations. One of the most important metrics in this domain is ...
GitHub

Python Toolkit for Uncertainty Quantification (PyTUQ)

The Python Toolkit for Uncertainty Quantification (PyTUQ) is a Python-only collection of libraries and tools designed for quantifying uncertainty in computational models. PyTUQ offers a range of UQ ...
ascopubs.org

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation

The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived ...
Nature

Application of the performance of machine learning techniques as support in the prediction of school dropout

School dropout data were considered because it has been studied from different approaches and whose figures indicate that there is work to be done 1. Dropping out of school can be defined as the ...
C&EN

Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Visual Studio Magazine

Gaussian Process Regression Using the scikit Library

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...
GitHub

Gaussian Process Regression and Forecasting Stock Trends

The aim of this project was to learn the mathematical concepts of Gaussian Processes and implement them later on in real-world problems - in adjusted closing price trend prediction consisted of three ...
C&EN

Development, Evaluation, and Application of Machine Learning Models for Accurate Prediction of Root Uptake of Per- and Polyfluoroalkyl Substances

Machine learning (ML) models were developed for understanding the root uptake of per- and polyfluoroalkyl substances (PFASs) under complex PFAS-crop-soil interactions. Three hundred root concentration ...
LinkedIn

Bayesian Estimation: A Practical Guide with Python and scikit-learn

In Python, we can perform Bayesian estimation using the scikit-learn library. Scikit-learn provides a BayesianRidge class that can be used to perform Bayesian linear regression. It uses a Bayesian ...
Frontiers

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Context: The third generation of cryptocurrencies gathers cryptocurrencies that are as diverse as the market is big (e.g., Dogecoin or Litecoin). While Dogecoin is seen as a memecoin, the other ...