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A Course in Probability and Statistics
This author's modern approach is intended primarily for honors undergraduates or undergraduates with a good math background taking a mathematical statistics or statistical inference course. The author takes a finite-dimensional functional modeling viewpoint (in contrast to the conventional parametric approach) to strengthen the connection between statistical theory and statistical methodology.
Brownian Motion and Classical Potential Theory
Brownian Motion and Classical Potential Theory is a six-chapter text that discusses the connection between Brownian motion and classical potential theory. The first three chapters of this book highlight the developing properties of Brownian motion with results from potential theory. The subsequent chapters are devoted to the harmonic and superharmonic functions, as well as the Dirichlet problem. These topics are followed by a discussion on the transient potential theory of Green potentials, with an emphasis on the Newtonian potentials, as well as the recurrent potential theory of logarithmic potentials. The last chapters deal with the application of Brownian motion to obtain the main theorems of classical potential theory. This book will be of value to physicists, chemists, and biologists.
Introduction to Probability Theory
Probability spaces; Combinatorial analysis; Discrete random variables; Expectation of discrete random variables; Continuous random variables; Jointly distributed random variables; Expectations and the central limit theorem; Moment generating functions and characteristic functions; Random walks and poisson processes.
Classification and Regression Trees
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
An Introduction to Stochastic Processes
Random walk; Markov chains; Poisson processes; Purely discontinuous markov processes; Calculus with stochastic processes; Stationary processes; Martingales; Brownian motion and diffusion stochastic processes.
