| 期刊全稱 | An Introduction to Markov Processes | | 影響因子2023 | Daniel W. Stroock | | 視頻video | http://file.papertrans.cn/156/155335/155335.mp4 | | 發(fā)行地址 | Includes supplementary material: | | 學(xué)科分類 | Graduate Texts in Mathematics | | 圖書封面 |  | | 影響因子 | To some extent, it would be accurate to summarize the contents of this book as an intolerably protracted description of what happens when either one raises a transition probability matrix P (i. e. , all entries (P)?j are n- negative and each row of P sums to 1) to higher and higher powers or one exponentiates R(P — I), where R is a diagonal matrix with non-negative entries. Indeed, when it comes right down to it, that is all that is done in this book. However, I, and others of my ilk, would take offense at such a dismissive characterization of the theory of Markov chains and processes with values in a countable state space, and a primary goal of mine in writing this book was to convince its readers that our offense would be warranted. The reason why I, and others of my persuasion, refuse to consider the theory here as no more than a subset of matrix theory is that to do so is to ignore the pervasive role that probability plays throughout. Namely, probability theory provides a model which both motivates and provides a context for what we are doing with these matrices. To wit, even the term "transition probability matrix" lends meaning to an otherwise rather peculiar set of hypothese | | Pindex | Textbook 20051st edition |
The information of publication is updating
|
|
|Archiver|手機版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-11 15:42
發(fā)表于 2025-3-21 18:02:32
