A Survey of Statistical Network Models
by A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi
Publisher: arXiv 2009
ISBN/ASIN: 1601983204
ISBN-13: 9781601983206
Number of pages: 96
Description:
We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation.
Download or read it online for free here:
Download link
(1.7MB, PDF)
Similar books
Reinforcement Learning: An Introductionby Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
(20982 views)
A First Encounter with Machine Learningby Max Welling - University of California Irvine
The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.
(6249 views)
Statistical Foundations of Machine Learningby Gianluca Bontempi, Souhaib Ben Taieb
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
(5374 views)
Gaussian Processes for Machine Learningby Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.
(22214 views)