An empirical analysis of entropy search in batch bayesian optimisation: A comprehensive study of function shape, batch size, noise level, and dimensionality impact on information-theoretic methods

Bayesian optimisation is a rapidly growing area of research that aims to identify the optimum of the black-box function, as it strategically directs the optimisation process towards promising regions. This paper provides an overview of the theoretical background used by the Entropy Search algorithms under study, mainly Predictive Entropy Search,...

Filtering Knowledge: A Comparative Analysis of Information-Theoretical-Based Feature Selection Methods

The data used in machine learning algorithms strongly influences the algorithms' capabilities. Feature selection techniques can choose a set of columns that meet a certain learning goal. There is a wide variety of feature selection methods, however, the ones we cover in this comparative analysis are part of the information-theoretical-based...

High Performance Computing and Information Theory with D-HYDRO 1D2D on Cloud Infrastructure

This study focuses on optimizing the use of high-performance computing on public cloud infrastructure, along with information theory, for assessing water systems. These assessments are computationally intensive and can benefit from parallel computing and the evaluation of the collected data with information theory. A case study of a water system...

A Bayesian framework for risk perception

We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy judgments are modeled by way of a Bayesian...

Multiple-Description Coding by Dithered Delta-Sigma Quantization

n-Channel Asymmetric Multiple-Description Lattice Vector Quantization