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Probability and Statistical Models in Operations Research, Computer and Management Sciences

This book explores the convergence of stochastic modeling, reliability tools, and the quest for solutions in an era of globalized challenges. The tools have become only more important in the unforeseen em…

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Daniel Sorensen
Statistical Learning in Genetics

This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, whe…

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A³N²M: Approximation, Applications, and Analysis of Nonlocal, Nonlinear Models

This volume collects papers based on plenary and invited talks given at the 50th Barrett Memorial Lectures on Approximation, Applications, and Analysis of Nonlocal, Nonlinear Models that was organized by …

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Janis E. Johnston, Kenneth J. Berry
Statistical Methods: Connections, Equivalencies, and Relationships

The primary purpose of this book is to introduce the reader to a wide variety of interesting and useful connections, relationships, and equivalencies between and among conventional and permutation statist…

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Peter E. Caines, David Levanony
Stochastic Lagrangian Adaptation

This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimatio…

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Stephen S. -T. Yau, Xiuqiong Chen, Yangtianze Tao, Jiayi Kang
Principles of Nonlinear Filtering Theory

This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, …

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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliabilit…

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Giovanni Cerulli
Fundamentals of Supervised Machine Learning

This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applicati…

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Hang Lee
Foundations of Applied Statistical Methods

This book covers methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports. These research activities rely on an adequate…

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Dan Crisan, Daniel Goodair
Stochastic Calculus in Infinite Dimensions and SPDEs

Introducing a groundbreaking framework for stochastic partial differential equations (SPDEs), this work presents three significant advancements over the traditional variational approach.Firstly, Stratonov…

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Guillaume Mercère
Data Driven Model Learning for Engineers

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate th…

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Emma Horton, Andreas E. Kyprianou
Stochastic Neutron Transport

This monograph highlights the connection between the theory of neutron transport and the theory of non-local branching processes. By detailing this frequently overlooked relationship, the authors provide …

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Sinho Chewi, Jonathan Niles-Weed, Philippe Rigollet
Statistical Optimal Transport

This monograph aims to offer a concise introduction to optimal transport, quickly transitioning to its applications in statistics and machine learning. It is primarily tailored for students and researcher…

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Thomas W. Macfarland
Introduction to Data Science in Biostatistics

Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies…

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More UK Success Stories in Industrial Mathematics

This volume highlights successful projects in which academic mathematicians in the United Kingdom got involved with solving problems in industry for which mathematics was essential. It contains a wide ran…

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George J. Knafl
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling

This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially …

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Robert H. Shumway, David S. Stoffer
Time Series Analysis and Its Applications

This 5th edition of this popular graduate textbook presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes numerous examples using n…

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Methodological and Applied Statistics and Demography II

This book of peer-reviewed short papers on methodological and applied statistics and demography is the second of four volumes from the 52nd Scientific Meeting of the Italian Statistical Society (SIS 2024)…

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Nan Chen
Stochastic Methods for Modeling and Predicting Complex Dynamical Systems

This second edition is an essential guide to understanding, modeling, and predicting complex dynamical systems using new methods with stochastic tools. Expanding upon the original book, the author covers …

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Computing Technologies and Applied Mathematics

This proceedings volume convenes revised, selected works presented at the 3rd International Seminar on Computing Technologies and Applied Mathematics (CTAM2024), held at Komsomolsk-na-Amure State Universi…

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