Kategoria BISAC: Mathematics >> Probability & Statistics - Bayesian Analysis

Exact sampling, specifically coupling from the past (CFTP), allows users to sample exactly from the stationary distribution of a Markov chain. During its nearly 20 years of existence, exact sampling has evolved into perfect simulation, which enables high-dimensional simulation from interacting distributions.

Perfect Simulation illustrates the application of perfect simulation ideas and algorithms to a wide range of problems. The book is one of the first to bring together research on simulation from statistics, physics, finance, computer science, and other areas into...

Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory, with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics, such as outlier tests, monitoring clinical trials, and using adaptive methods to make design changes based on accumulating data. The authors explain different methods of proofs and show how they are useful for establishing classic probability results.

After building a...

Developed from the author s course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear and nonlinear problems in biology, finance, geophysics, mechanics, chemistry, and other application areas. The text also thoroughly develops the problem of numerical integration and computation of expectation by the Monte-Carlo method.

The book begins with a history of Monte-Carlo...

Elementary Probability with Applications, Second Edition shows students how probability has practical uses in many different fields, such as business, politics, and sports. In the book, students learn about probability concepts from real-world examples rather than theory.

The text explains how probability models with underlying assumptions are used to model actual situations. It contains examples of probability models as they relate to:

• Bloc voting
• Population genetics
• Doubling strategies in casinos
• Machine...

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition, the book focuses on the formulation of appropriate statistical models to study problems in business, economics, and the social sciences, as well as on how to interpret the results from statistical analyses.

The book will be useful to students who are interested in rigorous applications of statistics to problems in business, economics and the social...

Introduction to Probability with Texas Hold em Examples illustrates both standard and advanced probability topics using the popular poker game of Texas Hold em, rather than the typical balls in urns. The author uses students natural interest in poker to teach important concepts in probability.

Due to the recent progress of communication tools and the globalization of the world economy and information space, our physical lives, economical existences, and information fields are becoming more and more interrelated. Many processes that were successfully modelled in the past by probability and statistical methods assuming independent behavior of the components are becoming more and more intertwined. Brownian motion and Newtonian mechanics are still utilized, but they often fail to serve as good models for many complicated systems with component interactions. This explains an...