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Neural Networks (NNs), particularly in their deep learning variants, have revolutionized pattern recognition and function approximation in high-dimensional spaces. They have emerged as powerful too...

When simulating random numbers from correspondingly parameterized statistical probability distributions, the pertaining statistical moments (if they exist and are finite) can be determined numerica...

When simulating random numbers from correspondingly parameterized statistical probability distributions, the pertaining statistical moments (if they exist and are finite) can be determined numerica...

The choice of a programming language is a critical decision in software development, as it impacts code quality, performance, maintainability, and scalability. This paper provides an analysis of 23...

The behavior of random variables under multiplicative combination is a topic of significance in diverse fields such as signal processing, finance, hydrology, wireless communications, Bayesian stati...

In probability theory and statistics, functions of random variables play a central role in modeling, simulation, and inference. While the addition and multiplication of random variables are often w...

The summation of random variables is central in probability theory and statistical modeling. Understanding how the sum of independent univariate distributions results in another known distribution ...

In modern software engineering, the selection of an appropriate programming language is not merely a technical choice but a strategic decision that influences system architecture, development metho...

To improve the performance of R programs, it is essential to optimize runtime and / or memory usage. In this study, the following performance optimization methods are focused on: oVectorization oUs...

Mathematical modeling constitutes a central pillar of modern science, providing indispensable tools for analyzing, predicting and optimizing complex real-world systems across a wide array of discip...

Numerical simulations are a cornerstone of modern scientific inquiry and technological development. They enable researchers to analyze complex systems that are analytically intractable or experimen...

R and Python are two of the most widely used programming languages for data science, machine learning, and statistical computing. In many scenarios, it is beneficial to integrate Python functionali...

Python and R are two of the most widely used programming languages in data science, statistics, and machine learning. While Python is known for its versatility and extensive libraries, R excels in ...

The increasing dominance of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning in data-driven disciplines has raised critical questions about the continuing relevance of traditio...

The subsequent note provides an analysis of some cycling behaviour with respect to a very simple doubling rule in combination with subtraction. Specifically this study will investigate the number o...