Logo

A Survey of Statistical Network Models

Large book cover: A Survey of Statistical Network Models

A Survey of Statistical Network Models
by

Publisher: arXiv
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.

Home page url

Download or read it online for free here:
Download link
(1.7MB, PDF)

Similar books

Book cover: Inductive Logic Programming: Theory and MethodsInductive Logic Programming: Theory and Methods
by - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(39399 views)
Book cover: Algorithms for Reinforcement LearningAlgorithms for Reinforcement Learning
by - Morgan and Claypool Publishers
We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.
(10536 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(24677 views)
Book cover: Boosting: Foundations and AlgorithmsBoosting: Foundations and Algorithms
by - 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.
(8359 views)