Libros de Matemáticas, página 23 de 470

This is the first textbook on pattern recognition to present the Bayesian viewpoint The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning No previous knowledge of pattern recognition or machine learning concepts is assumed Familiarity with multivariate calculus and basic linear algebra is required and some experience in the use of probabilities would be helpful though not essential as the book includes a self contained introduction to basic probability theory

This book avoids the traditional definition-theorem-proof format; instead a fresh approach introduces a variety of problems and examples all in a clear and informal style. The in-depth focus on applications separates this book from others, and helps students to see how linear algebra can be applied to real-life situations. Some of the more contemporary topics of applied linear algebra are included here which are not normally found in undergraduate textbooks. Theoretical developments are always accompanied with detailed examples, and each section ends with a number of exercises from which students can gain further insight. Moreover, the inclusion of historical information provides personal insights into the mathematicians who developed this subject. The textbook contains numerous examples and exercises, historical notes, and comments on numerical performance and the possible pitfalls of algorithms. Solutions to all of the exercises are provided, as well as a CD-ROM containing a searchable copy of the textbook.

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

Los dos tomos de este Calculus sirven muy adecuadamente como textos de dos primeros cursos en estudios que requieran una sólida base matemática, pues a las notables cualidades didácticas de un libro destinado a introducir al estudiante en el espiritu y practica de la Matematica basica, se añade la ponderacion y rigor logico en la exposicion de las teorias, lo que da caracter modelico a esta obra.

En esta décima edición de Ecuaciones diferenciales con aplicaciones de modelado los estudiantes de ingeniería y matemáticas hallarán abundantes ejemplos, explicaciones, recuadros, tablas, definiciones y ejemplos para el estudio analitico, cualitativo y cuantitativo de ecuaciones diferenciales. Aunadas al estilo directo, legible y provechoso del texto, estas caracteristicas hacen que Ecuaciones diferenciales con aplicaciones de modelado sea, y haya sido por años ya, parametro indiscutible, probado y accesible, entre los libros de texto para cursos de un semestre.Esta nueva edicion incluye una inedita y extensa seccion de proyectos con ejemplos de aplicaciones practicas para un sinfin de ecuaciones diferenciales. Adicionalmente, en esta entrega el autor amplia y mejora por completo varios capitulos del libro, ademas agrega y actualiza ejercicios, ejemplos, casos y definiciones en todas sus secciones.