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See below for a selection of the latest books from Mathematics category. Presented with a red border are the Mathematics books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Mathematics books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Presented in a comprehensive manner, this book provides a comprehensive foundation in algebraic approaches for the analysis of different types of social networks such as multiple, signed, and affiliation networks. The study of such configurations corresponds to the structural analysis within the social sciences, and the methods applied for the analysis are in the areas of abstract algebra, combinatorics, and graph theory. Current research in social networks has moved toward the examination of more realistic but also more complex social relations by which agents or actors are connected in multiple ways. Addressing this trend, this book offers hands-on training of the algebraic procedures presented along with the computer package multiplex, written by the book's author specifically to perform analyses of multiple social networks. An introductory section on both complex networks and for R will feature, however the subjects themselves correspond to advanced courses on social network analysis with the specialization on algebraic models and methods.
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Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other. The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications).
The book Numerical Methods for Inverse Problems consists of contemporaneouss articles featuring not only several well-known inverse problems used in the inference of many physical and engineering systems such as that of the partial differential equations but also the statistical and imaging inverse problems. It includes a variety of numerical methods for solving inverse problems such as that of the Tikhonov regularization; finite differences; and orthogonal decomposition; as well as those based on Bayesian inference, artificial neural networks, and quantum annealing.
The book Graphs:Theory and Algorithm is a collection of modern articles features several graph-based methods and algorithms. It also covers important theoretical aspects pertaining to matrix representations of graphs such as the Laplacian and distance matrices which can be used for solving problems such that of the Hamiltonian and shortest path, as well as in finding minimum spanning trees and matching patterns.
The book Handbook of Mixture Analysis is a collection of peer-reviewed articles featuring several statistical data analysis methods based on mixture models and their applications in scientific domains such as data mining, machine learning, physics, mechanical engineering, signal processing, economics, cosmology, computational medicine, and more. This book covers several aspects of mixture analysis and variety of models such as the Gaussian Mixture Model (GMM), Dirichlet processes Mixture Model (DMM), Poisson Mixture Regression Model (PMRM), Hierarchical Gamma Mixture Model (HGMM), Quadratic Mixture Model (QMM), K-fold Mixture Model (KMM), Finite Mixture Model (FMM), and Multi-partitions Subspace Mixture Model (M-SMM).
The book Fractal Analysis is a collection of contemporaneous articles aims to guide the reader through the world of fractals. A world of computer-generated self-similar patterns that can capture the intricacy of natural structure such as snowflakes, fern leaves, and tree branching. A world that is not only aesthetically pleasing but that is also at the basis of new technologies such as fractal antennas included in every mobile phone. The book is centred on simulation models as well as on the implementation of different methods of measuring the spatial and temporal characteristics of fractal patterns. For instance, box-counting based methods that can be used to determine the fractal dimension of the edges of the Saturn's rings, forested landscapes, brain imaging data, and occurrence of words in text. It also includes an overview of fractal theory concepts, from self-similarity to the complexity of fractal networks.
This book reviews the recent studies on the origin and evolution of atomic matter in the Universe, considering early Universe, interstellar regions, and the solar system. In particular, it focuses on the study of the Universe by spectroscopic observations, it examines the chemical history of the very early universe to the formation of first atoms, it treats of the creation of the higher elements in the heart of the stars, and it reviews the interstellar chemistry from the viewpoints of theory, experiments, models and observations. Moreover, it provides some examples of laboratory-based astrochemistry, and at last, it focuses on the evolutionary history of the moon and the inner solar system, and their Silica-rich volcanism.