Libros de Matemáticas, página 62 de 472

Covers the main theorems of algebraic number theory, including function fields over finite constant fields.

A Thorough Guide to Elementary Matrix Algebra and Implementation in R Basics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses, such as multivariate data analysis and linear models. It also covers advanced topics, such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices, for those who want to delve deeper into the subject. The book introduces the definition of a matrix and the basic rules of addition, subtraction, multiplication, and inversion. Later topics include determinants, calculation of eigenvectors and eigenvalues, and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques, including principal component analysis, canonical correlation analysis, and linear modeling. In addition to the algebraic manipulation of matrices, the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers. Avoiding vector spaces and other advanced mathematics, this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.

This book presents an introduction to the Finite Element Method (FEM) and is primarily aimed at students, engineers and scientists who already know structural calculation. It starts from the matrix reformulation of the direct stiffness method to understand the enormous potential of introducing the idea of the weak formulation for the approximation of strut structure problems. The correspondence of results between the initial classical formulation and that of the FEM when using linear shape functions and Hermite polynomials, makes it possible to approach its general formulation more easily as a procedure to obtain approximate solutions of differential equations in partial derivatives. It is essential now to introduce the idea of element, which allows, from the definition of a reduced number of base functions and through a simple combination process, to approximate its real behavior and, through superposition, that of the structure.

Ian Stewart, uno de los matemáticos más populares del mundo, autor de increíbles best seller como Locos por las matemáticas, vuelve a intrigarnos y a divertirnos con este libro maravilloso que nos descubre un paisaje matematico que ni habriamos imaginado. A lo largo de 20 capitulos, que pueden leerse independientemente, el profesor Stewart nos reta a resolver puzles y acertijos curiosisimos, algunos con importantes aplicaciones practicas, otros que han sido la pesadilla de los matematicos mas dotados hasta hace cuatro dias y todos ellos tan fascinantes como deliciosamente estrafalarios. No es solo que las matematicas puedan ser divertidas, es que, como nos demuestra Stewart, pueden explicarse en el lenguaje mas llano.

La Incapacidad Permanente, en sus diferentes graduaciones, nace como una prestación del Sistema Español de Seguridad Social, con la vocación de suplir el déficit de ingresos que sufre un trabajador cuando, por causa de enfermedad o accidente, ve reducida o anulada su capacidad laboral.- Análisis de la jurisprudencia actual.- Legislación aplicable.- Modelos administrativos y judiciales.- Prontuario para la tramitación de la prestación.- Destinado a Abogados, áreas de recursos humanos en las empresas y en general, personas interesadas en la gestión de la prestación.

Aiming to demystify the approach it explains how to parameterize and compare models while accounting for uncertainties in data model parameters and model structures The literature offers a bewildering variety of techniques Bayesian calibration data assimilation Kalman filtering model data fusion

Andrew GelmanThis graduate textbook introduces the fundamentals of Bayesian inference and modeling, describes methods for computing posterior distributions in hierarchical models, and explores a few standard linear regression and generalized linear models. The second edition adds chapters on nonlinear models and decision analysis.

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition * Four new chapters on nonparametric modeling * Coverage of weakly informative priors and boundary-avoiding priors * Updated discussion of cross-validation and predictive information criteria * Improved convergence monitoring and effective sample size calculations for iterative simulation * Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation * New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page.

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods in language ecologists can understand Unlike other books on the subject this one emphas