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Applied Bayesian Statistics
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build und...
Maps and Transcripts of the Ordinary World
"Maps and Transcripts of the Ordinary World is a collection of poems about memory, place, and distance between reality and its transcriptions"--
The Scientific Method
The surprising history of the scientific method—from an evolutionary account of thinking to a simple set of steps—and the rise of psychology in the nineteenth century. The idea of a single scientific method, shared across specialties and teachable to ten-year-olds, is just over a hundred years old. For centuries prior, science had meant a kind of knowledge, made from facts gathered through direct observation or deduced from first principles. But during the nineteenth century, science came to mean something else: a way of thinking. The Scientific Method tells the story of how this approach took hold in laboratories, the field, and eventually classrooms, where science was once taught as a ...
Spatially Explicit Hyperparameter Optimization for Neural Networks
Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Numerical Methods of Statistics
This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.
Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.
Eleanor, Eleanor, Not Your Real Name
Poetry. Winner of the 2008 Dorothy Brunsman Poetry Contest. "Here is a book addressed to origins: to first things; to first springs into the turbulence of real conversation. Cowles is a poet who knows where poetry comes from and whither it is bound. Hers is the adventure of tenderness brightly underway"--Donald Revell. "Kathyrn Cowles here touches on everything that is important to me ... In these pages, language is the field and the ammunition, it is the seriousness of the human world and the arrows that pierce that veil with funny and tender precision"--Eleni Sikelianos.
Beneath the Wreckage
It was supposed to be a summer we’d never forget. Instead, everything was stolen from me. The best friend who was more like a sister. The innocent way I looked at life. Leaving me with only unanswered questions. Returning to the island is the second chance I didn’t know I needed. And Hunter is the surprise that knocks me sideways. There’s a hurt in him that calls to my own. A strength I find in sharing our scars. Igniting a spark that turns to flame. But someone isn’t happy about the world I’m building for myself. And nothing will stop them from tearing it all apart…
Bayesian Analysis for the Social Sciences
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
Salary Book
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