Probability: theory and examples
This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2019
|
| Ausgabe: | Fifth edition. |
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
49 |
| Links: | https://doi.org/10.1017/9781108591034 |
| Zusammenfassung: | This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô's formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences. |
| Umfang: | 1 Online-Ressource (xii, 419 Seiten) |
| ISBN: | 9781108591034 |
Internformat
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| spelling | Durrett, Richard 1951- Probability theory and examples Rick Durrett Fifth edition. Cambridge Cambridge University Press 2019 1 Online-Ressource (xii, 419 Seiten) txt c cr Cambridge series in statistical and probabilistic mathematics 49 This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô's formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences. Erscheint auch als Druck-Ausgabe 9781108473682 |
| spellingShingle | Durrett, Richard 1951- Probability theory and examples |
| title | Probability theory and examples |
| title_auth | Probability theory and examples |
| title_exact_search | Probability theory and examples |
| title_full | Probability theory and examples Rick Durrett |
| title_fullStr | Probability theory and examples Rick Durrett |
| title_full_unstemmed | Probability theory and examples Rick Durrett |
| title_short | Probability |
| title_sort | probability theory and examples |
| title_sub | theory and examples |
| work_keys_str_mv | AT durrettrichard probabilitytheoryandexamples |