Objective: Based on the Bayesian network, this study investigates the impact pathways of multidimensional factors related to the living environment—specifically housing factors, exposure to daily ...

Kentaro Matsuura (2023). Bayesian Statistical Modeling with Stan, R, and Python. Singapore: Springer. URL: https://link.springer.com/book/10.1007/978-981-19-4755-1 ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Abstract: With the objective of enhancing the information security of traditional communication networks, an information security system based on an improved Bayesian network model was designed within ...

Abstract: Bayesian networks are widely used for causal discovery and probabilistic modeling across diverse domains including healthcare, multi-dimensional data analysis, environmental modeling, and ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

1 School of Computer Science, Technological University Dublin, Dublin, Ireland 2 ADAPT Research Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland Previous work ...

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 ...