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Generalized Linear Models
This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.
The Denison Family of Toronto
The Denisons were an unusual and colourful family. For over a century – from the War of 1812 to the eve of the Depression – they were in the forefront of political, military, social, and intellectual life in Toronto. They took their duties to king and country seriously, serving in public and military office, and established family colonies on their estates in Toronto. As the story of the family unfolds, it reveals the story of Toronto – the spirit of the times, the turbulence of politics, and the exciting growth of a new city. The Denison Family of Toronto focuses on George Denison III (1839-1925), military historian, senior police magistrate, and supporter of the Canada First and Imperial Federation movements. His story proves that Canada has produced some memorable individuals whose activities have for too long been obscured by historians' preoccupation with grander themes. But more than that, the history of the Denisons' quarrel with the United States and their flamboyant nationalism challenges the reader to examine his own assumptions about the Canadian identity.
A Record of the Descendants of Capt. George Denison, of Stonington, Conn. With Notices of his Father and Brothers, and Some Account of Other Denisons who Settled in America in the Colony Times
Reprint of the original, first published in 1881.
Bayesian Statistics and Its Applications
In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.
The Royal Navy List
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Bayesian Methods for Nonlinear Classification and Regression
Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.
Technometrics
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