Data Mining with Rattle and R

Sample

C. Radhakrishna Rao, D.N. Shanbhag

The ICFE was originally introduced to characterize a probability distribution by some invariant property under a stochastic change (damage) to the original random variable, and it is a generalization of a certain version of the Choquet-Deny convolution equation which occurs in potential theory. The solution of the ICFE is obtained using certain properties of exchangeable random elements or martingales, amongst other things. The solutions to these functional equations provide a unified and elegant approach to characterizations of the exponential, geometric, Pareto, Weibull, stable, Poisson and other distributions under a variety of stochastic properties of the random variable. The ICFE also plays an important role in renewal processes, potential theory and other applications of stochastic processes. Several illustrative examples are given to show the wide applicability of the ICFE. Besides the general theory associated with the ICFE and related equations, the book introduces new probability tools and techniques which should be of interest to research workers in probability and statistics, as well as those working in other areas such as biology, medicine and engineering.

Sample

Joseph M. Hilbe

"This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation, and evaluation. Complete Stata and R code are provided throughout the text, with additional code (plus SAS), derivations, and data provided on the book's website. Written for the practicing researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data"--

Sample

Areski Nait Abdallah

This book presents the foundations of reasoning with partial information and a theory of common sense reasoning based on monotonic logic and partial structures. This theory was designed specifically for the needs of practicing computer scientists and provides easily implementable algorithms. Starting from first principles, following the logic of discovery of Karl Popper and Imre Lakatos, and the semantics of programming languages, the book develops a system of reasoning with partial information, and applies it to a comprehensive study of the problem examples from the literature of common sense reasoning. Proof-theoretic and model-theoretic views are considered in the applications, as well as logical problems of theoretical physics, such as issues related to Heisenberg's uncertainty principle. The book points out that customary expositions of common-sense reasoning are based on a flawed non-monotonic reasoning paradigm and that the resulting solutions proposed for major problems, such as the frame problem, are either ad hoc or inadequate. It is shown that non-monotonicity results from hiding information that should not be hidden. The essential research in common-sense reasoning has been developed in isolation from the disciplines of theoretical computer science and classical logic. This work breaks the isolation and establishes deep links. The book will be of interest to computer scientists, mathematicians, logicians, and philosophers interested in the foundations and applications of reasoning with partial information.

Sample

Mary Kane and William M.K. Trochim

This is a complete guide to the concept mapping methodology and strategies behind using it for a broad range of social scientists - including students, researchers and practitioners.