Stochastics

This study discusses the history of the central limit theorem and related probabilistic limit theorems from about 1810 through 1950.

Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems.

Classical probability theory provides information about random walks after a fixed number of steps.

While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting.

This book aims to give a user friendly tutorial of an interdisciplinary research topic (fronts or interfaces in random media) to senior undergraduates and beginning grad uate students with basic knowledge of partial differential equations (PDE) and prob ability.

In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies.

In recent years there has been an explosion of network data - that is, measu- ments that are either of or from a system conceptualized as a network - from se- ingly all corners of science.

Biometric  System and Data Analysis: Design, Evaluation, and Data Mining brings together aspects of statistics and machine learning to provide a comprehensive guide to evaluate, interpret and understand biometric data.

This second edition presents up-to-date material on the theory of weak convergance of convolution products of probability measures in semigroups, the theory of random walks on semigroups, and their applications to products of random matrices.

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.

Chance continues to govern our lives in the 21st Century.

This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level.

influence on the conclusions that result.

Many aspects of phenomena critical to our lives can not be measured directly.

Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?

Financial globalization has increased the significance of methods used in the evaluation of country risk, one of the major research topics in economics and finance.

This book contains an introduction to three topics in stochastic control: discrete time stochastic control, i.

This book developed out of my year-long course on asymptotic theory at Purdue University.

The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics.

Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications.

Extremes Values, Regular Variation and Point Processes is a readable and efficient account of the fundamental mathematical and stochastic process techniques needed to study the behavior of extreme values of phenomena based on independent and identically distributed random variables and vectors.

Stochastic di?

ThisresearchmonographdevelopstheHamilton-Jacobi-Bellman(HJB)theory viathedynamicprogrammingprincipleforaclassofoptimalcontrolproblems for stochastic hereditary di?

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

Our basic question is: Given a collection of DNA sequences, what underlying forces are responsible for the observed patterns of variability?

From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career.

Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective.

This IMA Volume in Mathematics and its Applications TOPICS IN STOCHASTIC ANALYSIS AND NONPARAMETRIC ESTIMATION contains papers that were presented at the IMA Participating Institution conference on "e;Asymptotic Analysis in Stochastic Processes, Nonparamet- ric Estimation, and Related Problems"e; held on September 15-17, 2006 at Wayne State University.

The main intended audience for this book is undergraduate students in pure and applied sciences, especially those in engineering.

The goal of this book is to introduce statisticians, and other researchers with a background in mathematical statistics, to empirical processes and semiparametric inference.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics.

Computational probability is a set of stochastic methods that has emerged over the past decade that allow researchers and students to solve problems that require exact probability calculations previously considered arduous or intractable.

Exceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios.

The present volume analyzes mathematical models of time-dependent physical p- nomena on three levels: microscopic, mesoscopic, and macroscopic.

The idea of writing this bookarosein 2000when the ?

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty.

Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scienti?

As a student I discovered in our library a thin booklet by Frederick Mosteller entitled50 Challenging Problems in Probability.

There can be no question, my dear Watson, of the value of exercise before breakfast.

The book of nature, according to Galilei, is written in the language of mat- matics.

Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data.

A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement.

Most commonly-used parametric and permutation statistical tests, such as the matched-pairs t test and analysis of variance, are based on non-metric squared distance functions that have very poor robustness characteristics.

Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths.

Of what use is mathematics?