Mathematics of probability
QA274 .S854 2013
- Unknown QA274 .S854 2013
- Includes bibliographical references (pages 279) and index.
- Some background and preliminaries
- Probability theory on uncountable sample spaces
- Some applications to probability theory
- The central limit theorem and Gaussian distributions
- Discrete parameter stochastic processes
- Some continuous-time processes
- Publisher's Summary
- This book covers the basics of modern probability theory. It begins with probability theory on finite and countable sample spaces and then passes from there to a concise course on measure theory, which is followed by some initial applications to probability theory, including independence and conditional expectations. The second half of the book deals with Gaussian random variables, with Markov chains, with a few continuous parameter processes, including Brownian motion, and, finally, with martingales, both discrete and continuous parameter ones. The book is a self-contained introduction to probability theory and the measure theory required to study it.
(source: Nielsen Book Data)
- Stochastic processes.
- Probability theory and stochastic processes > Foundations of probability theory > None of the above, but in this section.
- Probability theory and stochastic processes > Markov processes > Markov chains (discrete-time Markov processes on discrete state spaces)
- Probability theory and stochastic processes > Markov processes > None of the above, but in this section.
- Probability theory and stochastic processes > Stochastic processes > Martingales with discrete parameter.
- Probability theory and stochastic processes > Stochastic processes > Martingales with continuous parameter.
- Publication date
- Daniel W. Stroock.
- Graduate studies in mathematics ; volume 149