Libros de estadística y probabilidad, página 37 de 120

Geostatistical Functional Data AnalysisExplore the intersection between geostatistics and functional data analysis with this insightful new referenceGeostatistical Functional Data Analysis presents a unified approach to modelling functional data when spatial and spatio-temporal correlations are present. The Editors link together the wide research areas of geostatistics and functional data analysis to provide the reader with a new area called geostatistical functional data analysis that will bring new insights and new open questions to researchers coming from both scientific fields. This book provides a complete and up-to-date account to deal with functional data that is spatially correlated, but also includes the most innovative developments in different open avenues in this field.

A brilliant account of how models are so often abused and of how they should be used' John KayHow do mathematical models shape our world - and how can we harness their power for good?Models are at the centre of everything we do. Whether we use them or are simply affected by them, they act as metaphors that help us better understand the increasingly complex problems facing us in the modern world. Without models, we couldn't begin to tackle three of the major challenges facing modern society: regulation of the economy, climate change and the COVID-19 pandemic. Yet in recent years, the validity of the models we use has been hotly debated and there has been renewed awareness of the disastrous consequences when the makers and interpreters of models get things wrong.Drawing on contemporary examples from finance, climate and health policy, Erica Thompson explores what models are, why we need them, how they work and what happens when they go wrong. This is not a book that argues we should do away with models, but rather, that we need to properly understand how they are constructed - and how some of the assumptions that underlie the models we use can have significant unintended consequences. Unexpectedly humorous, thought-provoking and passionate, this is essential reading for everyone.

En este libro se desarrollan los conceptos fundamentales de la Estadística y la Probabilidad, al nivel adecuado para alumnos de primer curso de universidad. Podemos distinguir tres grandes bloques: el primero esta dedicado a la Estadistica descriptiva e incluye un repaso de la teoria elemental de conjuntos, junto con un estudio de las medidas de centralizacion y dispersion, la covarianza y la regresion lineal; se remata este bloque con una introduccion a la combinatoria. El segundo bloque trata la teoria de probabilidades, desde el nivel mas elemental (sucesos) hasta el estudio de las variables aleatorias, tanto discretas como continuas, unidimensionales y bidimensionales, con atencion especial a los tipos mas importantes: binomial, de Poisson, geometrica, exponencial y normal. Cabe destacar la inclusion de la funcion caracteristica entre las secciones de estos capitulos. Este bloque se cierra con una version sencilla, pero suficiente, del teorema central del limite. La inferencia estadistica comprende tanto la estimacion como el contraste de hipotesis. Cobran especial relevancia aqui la distribucion de probabilidad normal, la t de Student, la F de Fisher y la ?2 de Pearson. Cada capitulo contiene ejemplos ilustrativos y ejercicios resueltos o propuestos.

This book provides a lively and accessible introduction to the numerical solution of stochastic differential equations with the aim of making this subject available to the widest possible readership. It presents an outline of the underlying convergence and stability theory while avoiding technical details. Key ideas are illustrated with numerous computational examples and computer code is listed at the end of each chapter.The authors include 150 exercises, with solutions available online, and 40 programming tasks. Although introductory, the book covers a range of modern research topics, including Ito versus Stratonovich calculus, implicit methods, stability theory, nonconvergence on nonlinear problems, multilevel Monte Carlo, approximation of double stochastic integrals, and tau leaping for chemical and biochemical reaction networks. An Introduction to the Numerical Simulation of Stochastic Differential Equations is appropriate for undergraduates and postgraduates in mathematics, engineering, physics, chemistry, finance, and related disciplines, as well as researchers in these areas.The material assumes only a competence in algebra and calculus at the level reached by a typical first-year undergraduate mathematics class, and prerequisites are kept to a minimum. Some familiarity with basic concepts from numerical analysis and probability is also desirable but not necessary.

Larson/Edwards'' CALCULUS, 11th Edition, expertly combines the printed textbook and technology to deliver everything you need to master the material and pass the class. Stepped-out solution videos with instruction are available at CalcView.com for selected exercises throughout the text, and the website CalcChat.com presents free solutions to all of the odd-numbered exercises in the text.