Libros de estadística y probabilidad, página 17 de 115

El objetivo de este libro es cambiar nuestra manera de entender el mundo y la información que día a día procesamos. En lugar de ser esclavos de los datos, este libro nos dota de las herramientas básicas (algunas de ellas muy sencillas) para aprender a preguntar "¿Por que?" y tomar las mejores decisiones posibles. Un libro que ya es esencial en todos los cursos de empresariales, economia, psicologia, medicina y filosofia.¿Alguna vez te has interesado por los rompecabezas y el misterio de las causas y los efectos? En este libro encontraras respuestas que iluminaran tu camino. Ademas, es entretenidisimo. DANIEL KAHNEMAN (autor de Pensar rapido, pensar despacio).

Brian Clegg was always fascinated by Isaac Asimovs classic Foundation series of books, in which the future is predicted using sophisticated mathematical modeling of human psychology and behavior. Only much later did he realize that Asimovs "psychohistory" had a real-world equivalent: game theory.Originating in the study of probabilistic gambling games that depend on a random sourcethe throw of a dice or the toss of a coingame theory soon came to be applied to human interactions: essentially, what was the best strategy to win whatever you were doing? Its mathematical techniques have been applied, with varying degrees of wisdom, to fields such as economics, evolution, and questions such as how to win a nuclear war. Clegg delves into game theorys colorful history and significant findings and shows what we can all learn from this oft-misunderstood field of study.

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

Many modelling and control of two link flexible manipulators are presented in this book The lumped parameter modelling method and the assumed modes method modelling are reviewed comprehensively The book also reviews the trajectory tracking problem and tip trajectory tracking problem along with the suppression of tip deflection of the links An exponential time varying signal and a chaotic signal are considered as the desired trajectories The identical non identical slave manipulator is synchronised with the controlled master manipulator so that the slave manipulator indirectly follows the desired manipulator

El objetivo principal de esta obra es brindar un manual comprensible de las tablas y fórmulas importantes que normalmente se utilizan en cursos de estadística elemental e intermedia, con la correspondiente explicacion para utilizarlas, con lo que se pretende que profesores y otros usuarios de la estadistica encuentren en ella una valiosa referencia independiente. Esta obra surge como una necesidad, experimentada en la practica de clases de los autores, al darse cuenta de las dificultades que enfrentaban los estudiantes al utilizar tablas y formulas estadisticas para la resolucion de problemas, debido a que las tablas que se hallan en los apendices de los libros de texto, generalmente son limitadas o no estan en español. Ahora, todo profesor, estudiante o profesionista puede tener acceso facil y rapido a las tablas y formulas estadisticas, asi como una guia para su uso y aplicacion. Una obra que no debe faltar en la biblioteca de toda persona involucrada con la estadistica.

Este libro es una puerta a la Estadística Aplicada con R. En el se explican Metodos Básicos de Estadística y se introduce al lector en el paquete estadístico gratuito R, para usuarios que no tengan ningun conocimiento previo de dicho programa. Se enseña como ejecutar dichos metodos estadisticos con R, incluyendo numerosos ejemplos practicos, con datos reales o con datos que podrian llegar a ser reales (nonfiction problems).

The book is written to be accessible and useful to those with no prior experience of Python but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book This book is written in such a way that it can also serve as a selfcontained handbook for professionals working in various fields

mlr3 is an award winning ecosystem of R packages that have been developed to enable state of the art machine learning capabilities in R This book gives an overview of flexible and robust machine learning methods with an emphasis on how to implement them using mlr3 in R

Based on their extensive experience with teaching R and statistics to applied scientists the authors provide a beginner s guide to R To avoid the difficulty of teaching R and statistics at the same time statistical methods are kept to a minimum The text covers how to download and install R import and manage data elementary plotting an introduction to functions advanced plotting and common beginner mistakes This book contains everything you need to know to get started with R

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

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies The problems are becoming larger and with growing complexity Such problems are becoming cumbersome when handled by traditional optimization methods This has motivated researchers to resort to artificial intelligence AI based nature inspired solution methodologies or algorithms The Handbook of AI based Metaheuristics provides a wide ranging reference to the theoretical and mathematical formulations of metaheuristics including bio inspired swarm based socio cultural and physics based methods or algorithms their testing and validation along with detailed illustrative solutions and applications and newly devised metaheuristic algorithms This will be a valuable reference for researchers in industry and academia as well as for all Master s and PhD students working in the metaheuristics and applications domains

This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning Moreover it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning The resulting paradigm called deep generative modeling utilizes the generative perspective on perceiving the surrounding world It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions i e how events occur and in what order The adjective deep comes from the fact that the distribution is parameterized using deep neural networks There are two distinct traits of deep generative modeling First the application of deep neural networks allows rich and flexible parameterization of distributions Second the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning Moreover probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions Deep Generative Modeling is designed to appeal to curious students engineers and researchers with a mode