**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

**Machine Learning and Data Mining: Lecture Notes**

by

**Aaron Hertzmann**-

**University of Toronto**

Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.

(

**7322**views)

**Boosting: Foundations and Algorithms**

by

**Robert E. Schapire, Yoav Freund**-

**The MIT Press**

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.

(

**4121**views)

**A Course in Machine Learning**

by

**Hal DaumÃ© III**-

**ciml.info**

Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.

(

**17068**views)

**Introduction to Machine Learning**

by

**Alex Smola, S.V.N. Vishwanathan**-

**Cambridge University Press**

Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.

(

**6496**views)