GitHub

psoerensen/bayesian-linear-regression

This repository includes theoretical notes, slides, and hands-on R examples for exploring Bayesian Linear Regression. It introduces both classical and Bayesian regression methods, showing how to ...
Hosted on MSN

Linear Regression in Machine Learning: Explained Clearly with Real Examples

Discover a smarter way to grow with Learn with Jay, your trusted source for mastering valuable skills and unlocking your full potential. Whether you're aiming to advance your career, build better ...
SciELO

AN EFFICIENT COUNT DATA MODEL: PROPERTIES, ACTUARIAL MEASURES, BAYESIAN ESTIMATION, REGRESSION MODEL, AND APPLICATIONS TO HEALTH CARE DATA

Based on the compounding mechanism, a unique discrete probability distribution is investigated in this paper. The Poisson distribution is mixed with a lifetime model called as the Fav-Jerry model. The ...
IEEE

Bayesian Ridge Regression-Based Graph Injection Attack on IIoT

Abstract: The systems within the Industrial Internet of Things (IIoT) have complex structures and non-Euclidean data, which are challenging to manage. Due to the advantages of graph neural networks ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
Visual Studio Magazine

Naive Bayes Regression Using C#

The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...
Visual Studio Magazine

Naive Bayes Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
marktechpost

Embed-then-Regress: A Versatile Machine Learning Approach for Bayesian Optimization Using String-Based In-Context Regression

Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces.
marktechpost

GraphIC: A Novel Machine Learning Approach that Leverages Graph-based Representations of Reasoning Processes Coupled with Bayesian Networks (BNs) to Select In-Context Examples ...

In-context learning (ICL) enables LLMs to adapt to new tasks by including a few examples directly in the input without updating their parameters. However, selecting appropriate in-context examples ...