This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering.The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. T
Elementary Applications of Probability Theory: With an introduction to stochastic differential equations
โ Scribed by Henry C. Tuckwell (auth.)
- Publisher
- Springer US
- Year
- 1995
- Tongue
- English
- Leaves
- 307
- Series
- Chapman & Hall Statistics Textbook Series
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Content:
Front Matter....Pages i-xv
A review of basic probability theory....Pages 1-15
Geometric probability....Pages 16-29
Some applications of the hypergeometric and Poisson distributions....Pages 30-60
Reliability theory....Pages 61-80
Simulation and random numbers....Pages 81-97
Convergence of sequences of random variables: the central limit theorem and the laws of large numbers....Pages 98-122
Simple random walks....Pages 123-147
Population genetics and Markov chains....Pages 148-182
Population growth I: birth and death processes....Pages 183-203
Population growth II: branching processes....Pages 204-218
Stochastic processes and an introduction to stochastic differential equations....Pages 219-236
Diffusion processes, stochastic differential equations and applications....Pages 237-284
Back Matter....Pages 285-292
๐ SIMILAR VOLUMES
This is an introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. Based on measure theory, which is introduced as smoothly as possible, it provides practical skills in the use of MAPLE in the context of probabi
<p>The authors provide a fast introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. The book is based on measure theory which is introduced as smoothly as possible. It is intended for advanced undergraduate st
This is an introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. Based on measure theory, which is introduced as smoothly as possible, it provides practical skills in the use of MAPLE in the context of probabi