000			a
999			_c30884 _d30884
008			220627b xxu||||| |||| 00| 0 eng d
020			_a9781108473682
082			_a519.2 _bDUR
100			_aDurrett, Rick
245			_aProbability : theory and examples
250			_a5th ed.
260			_bCambridge University Press, _c2019 _aCambridge :
300			_axii, 419 p. ; _bill., _c27 cm
365			_b62.99 _cGBP _d100.50
490			_aCambridge series in statistical and probabilistic mathematics ; _vv.49
504			_aIncludes bibliographical references and index.
520			_aThis 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. Collapse summary
650			_aProbabilities
942			_2ddc _cBK