General

The Memoirs of the AMS is devoted to the publication of new research in all areas of pure and applied mathematics. The Memoirs is designed particularly to publish long papers of groups of cognate papers in book form, and is under the supervision of the...

Graph Neural Networks (GNNs) have revolutionized the way we learn representations from graph-structured data, becoming a cornerstone for applications in social networks, recommendation systems, biology, and beyond. However, mainstream GNNs rely heavily...

This book provides an introduction to double generalized linear models (DGLMs) under frequentist and Bayesian frameworks. These models include the class of generalized linear models and compose a unified class of models, where appropriate functions of...

Phylogenetics is a topical and growing area of research. Phylogenies (phylogenetic trees and networks) allow biologists to study and graph evolutionary relationships between different species. These are also used to investigate other evolutionary...

From the Ising model to large language models, Gibbs Measures in Machine Learning offers a complete journey through one of the most powerful concepts connecting statistical physics and modern AI.Starting with the mathematical foundations - measure...

ELEMENTARY SURVEY SAMPLING is an engaging and practical introduction to survey sampling. The book begins with real-world scenarios and uses easy-to-understand formulas, methodologies, and concepts to show how sample surveys are used in a wide variety of...

Theory, experiments, computation, and data are considered as the four pillars of science and engineering. Experimental Design for Data Science and Engineering describes efficient statistical methods for making the experiments cheaper and computations...

This thoroughly revised and updated new edition reflects the progress that has been made in dose-exposure-response (DER) modeling. As the title suggests, the new edition covers more topics on dose and dose adjustment. A large part of the book has been...

Hidden Semi-Markov Models (HSMMs) have been extensively used for diverse applications where the objective is to analyze time series whose dynamics can be explained by a hidden process. A Comprehensive Guide to HSMM offers an accessible introduction to...

The presented ideas create and shape the vision of a "Manifesto of Higher Education Governance".Fractals are typically understood as self-similar structures, but this concept is expanded to include "self-different fractals". These fractals align with a...

This book offers a comprehensive exploration of popular continuous distribution families, including Exponentiated, Beta, Kumaraswamy, T-X, and Transmuted. It details methods for developing new distribution families, their properties, and inference...

Predicting the future is a difficult task but, as with the weather, it is possible with good models. But how does one predict the far future before the near future is known? Time parallel time integration, also known as PinT (Parallel-in-Time) methods,...

This book provides a guide for improving test statistics that are based on phi-divergence for discrete models, which include various kinds of independence models of contingency tables as well as generalized linear models of binary data. The improvements...

This book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to the computation of average hitting times and...

Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in...

Statistics in Environmental Monitoring and Assessment details how statistics plays an essential role in every step of an environmental monitoring and assessment program, from the sampling design to the data management and data analysis methods that are...

Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support...

The primary purpose of this book is to introduce the reader to elementary statistical methods as related to tests of differences, such as one-sample tests, tests of two independent samples, tests of two matched samples, and one-way analysis of variance...

Implementing R for Statistics provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core...

Mathematical Foundations of Artificial Intelligence: Basics of Manifold Theory is the first volume in a two-part series. Together, they establish a unifying mathematical framework based on smooth manifold theory and Riemannian geometry essential tools...

This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning...

The purpose of these notes is to explore some simple relations between Markovian path and loop measures, the Poissonian ensembles of loops they determine, their occupation fields, uniform spanning trees, determinants, and Gaussian Markov fields such as...

Advanced Forecasting with Python, Second Edition, is a comprehensive and practical guide to mastering modern forecasting techniques using Python. Designed for data scientists, analysts, and machine learning practitioners, this updated edition bridges the...

This book provides fundamental concepts and algorithms of the Hidden Markov Model (HMM) and its applications in finance, such as stock price predictions, and other areas such as speech recognition. Their wide range uses make HMMs very attractive to...

An accessible and authoritative introduction to data handling and analysis for biology students—requiring no previous experience of statistics.

An accessible and authoritative introduction to data handling and analysis for biology students—requiring no previous experience of statistics.

Most data arrive with more than two numeric variables which means that plotting it on a computer screen or printed page presents a challenge: how do you visually explore for associations between more than two variables? Visualising data provides the...

Queuing theory methods are employed in managing traffic flows, shipping facilities, communication systems, vertical transportation, and others. The study of such systems is outside the framework of the methods of classical queuing theory; thus, new...

This volume constitutes the proceedings of First International Conference on Modeling, Simulation and Computer Technology, ICMSCT 2024, held in Skikda, Algeria, during November 5-6, 2024. The 45 full papers presented in this volume were carefully...

This volume provides a comprehensive introduction to the principles and practices of experimental design, a cornerstone of empirical research across disciplines. Covering foundational topics such as sampling, randomization, and analysis of variance,...

This volume addresses the complexities of real-world data analysis, offering clear guidance through common challenges such as missing data, model specification, variable selection, and hypothesis testing. It also introduces tools for deeper...

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented...

The description for this book, Stationary Processes and Prediction Theory. (AM-44), will be forthcoming.Book InformationISBN 9780691080413Author Harry FurstenbergFormat PaperbackPage Count 296Imprint Princeton University PressPublisher Princeton...

An Introduction to Stochastic Modeling, Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes...

This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and...

Predictive Modelling for Football Analytics discusses the most well-known models and the main computational tools for the football analytics domain. It further introduces the footBayes R package that accompanies the reader through all the examples...

This textbook is the second edition of the linear algebra and optimization book that was published in 2020. The exposition in this edition is greatly simplified as compared to the first edition. The second edition is enhanced with a large number of...

This CCIS 2538 volume constitutes the proceedings of First and Second International Conference on Advancements in Machine Learning, ICCAML 2024, in Pune, India, during February 28-29, 2024. The 19 full papers are carefully reviewed and selected from 173...

This book provides a self-contained lecture on a Malliavin calculus approach to asymptotic expansion and weak approximation of stochastic differential equations (SDEs), along with numerical methods for computing parabolic partial differential equations...

Most data arrive with more than two numeric variables which means that plotting it on a computer screen or printed page presents a challenge: how do you visually explore for associations between more than two variables? Visualising data provides the...