| 000 | a | ||
|---|---|---|---|
| 999 |
_c29771 _d29771 |
||
| 008 | 191121b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9781138630154 | ||
| 082 |
_a003 _bCRA |
||
| 100 | _aCrane, Harry | ||
| 245 | _aProbabilistic foundations of statistical network analysis | ||
| 260 |
_aBoca Raton _bChapman and Hall/CRC _c2018 |
||
| 300 |
_axx, 236 p. _bill. _c24 cm. |
||
| 365 |
_b38.99 _cGBP _d93.50 |
||
| 490 |
_aMonographs on statistics and applied probability _vv. 157 |
||
| 504 | _aIncludes bibliographical references and index. | ||
| 520 | _aProbabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author's incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. | ||
| 650 | _aStatistical methods | ||
| 650 | _aNetwork analysis | ||
| 650 | _aSystem analysis | ||
| 650 | _aMathematical models | ||
| 650 | _aProbabilities | ||
| 942 |
_2ddc _cBK |
||