Probability and statistics

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains.

Multiscale geographically weighted regression (MGWR) is an important method that is used across many disciplines for exploring spatial heterogeneity and modeling local spatial processes.

Multiscale geographically weighted regression (MGWR) is an important method that is used across many disciplines for exploring spatial heterogeneity and modeling local spatial processes.

This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems.

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals.

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals.

This book provides an overview of statistical concepts and basic methodology for the study of genetics of human traits and diseases.

This book provides an overview of statistical concepts and basic methodology for the study of genetics of human traits and diseases.

Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery.

This book offers a comprehensive examination of the Cramer-Lundberg model, which is the most extensively researched model in ruin theory.

This book explores the multiple facets of habit from diverse and complementary theoretical frameworks.

Complex dynamics is one of the most fascinating subjects of study and research in mathematics.

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data.

Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society.

This book not only presents essential material to understand fuzzy metric fixed point theory, but also enables the readers to appreciate the recent advancements made in this direction.

This volume provides an overview of two of the most important examples of interacting particle systems, the contact process, and the voter model, as well as their many variants introduced in the past 50 years.

This book provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence.

Change point analysis is a crucial statistical technique for detecting structural breaks within datasets, applicable in diverse fields such as finance and weather forecasting.

This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data.

Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications.

This book explains the best practices of the UNCTAD & WTO for trade analysis to the R users community.

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease.

Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach, Volume 2 covers several important topics that were not included in the first volume.

'Eye-opening.

The book offers insight into the healthcare system by exploring emerging technologies and AI-based applications and implementation strategies.

This book is a guide to non-academic careers for quantitative social scientists.

This book provides an introduction to computer-based methods for the analysis of genomic data.

This book presents the foundations of using analytics from the laboratory, social media platforms, and the web.

This book summarises for the first time all relevant methodologies for type-based flood statistics, introduces the basis of flood typology and makes them accessible to the user.

This book is a guide to non-academic careers for quantitative social scientists.

Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach, Volume 2 covers several important topics that were not included in the first volume.

Models for Non-Modelers focuses not on how to design models but on how to understand and critically appraise them.

Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications.

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called "e;ADAM"e; (Augmented Dynamic Adaptive Model).

This book explains the best practices of the UNCTAD & WTO for trade analysis to the R users community.

This book addresses the description and analysis of occurrence data frequently encountered in epidemiological studies.

This book summarises for the first time all relevant methodologies for type-based flood statistics, introduces the basis of flood typology and makes them accessible to the user.

For thousands of years, music has acted as a powerful medium for evoking emotions, facilitating communication, and nurturing overall well-being.