This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The fourth edition begins with a short chapter on measure theory to orient readers new to the subject.
'The author has done an extraordinary job in showing not simply what the presented theorems can be used for, but also what they cannot be used for.'
Miklós Bóna Source: SIGACT News
Loading metrics...
* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.
Usage data cannot currently be displayed.
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.
Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.