Abstract: The article introduces multidimensional Bayesian networks (MBNs), an advanced extension of traditional Bayesian networks (BNs) and object-oriented BNs (OOBNs). OOBNs effectively modularize ...

RLC circuit modeling and simulation using Python, explained step by step. Explore resonance, damping, and frequency response with practical coding and clear physics insights. #RLCCircuit ...

Background: Understanding how different modeling strategies affect associations in nutritional epidemiology is critical, especially given the temporal complexity of dietary and health data. Objective: ...

Dive into the world of physics simulations with this AstroBlaster collision modeling tutorial using Python! 🚀💥 In this video, we break down how to simulate space collisions, from basic physics ...

Volatility forecasting is a key component of modern finance, used in asset allocation, risk management, and options pricing. Investors and traders rely on precise volatility models to optimize ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

Version of Record: This is the final version of the article. This work proposes a new approach to analyse cell-count data from multiple brain regions. Collecting such data can be expensive and ...

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 ...

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...