Hierarchical longitudinal models of relationships in social networks
نویسندگان
چکیده
منابع مشابه
Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models
In this paper, we first define longitudinal-dynamic heteroscedastic hierarchical normal models. These models can be used to fit longitudinal data in which the dependency structure is constructed through a dynamic model rather than observations. We discuss different methods for estimating the hyper-parameters. Then the corresponding estimates for the hyper-parameter that causes the association...
متن کاملOn MCMC sampling in hierarchical longitudinal models
Markov chain Monte Carlo MCMC algorithms have revolutionized Bayesian practice In their simplest form i e when parameters are updated one at a time they are however often slow to converge when applied to high dimensional statistical models A remedy for this problem is to block the parameters into groups which are then updated simultaneously using either a Gibbs or Metropolis Hastings step In th...
متن کاملEpidemics in Hierarchical Social Networks
Epidemiological processes are studied within a recently proposed social network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population of individuals may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H...
متن کاملfaculty of psychology and social sciences group of anthropology master thesis in major of anthropology
چکیده پایان نامه (شامل خلاصه، اهداف، روش های اجرا و نتایج به دست آمده): کار جمع آوری گو یش های محلی در سال های اخیر شتاب امیدوار کننده ای به خود گرفته است. شاید از بارزترین اهداف جمع آوری گویش های مختلف، ثبت و ضبط آن، جلوگیری از نابودی و مهمتر از همه حل مشکلات دستوری زبان رسمی باشد. دقت در فرآیند های زبانی گویش های محلی نوع ارتباط مردم نواحی مختلف با پیرامون نشان را به ما نشان خواهد داد. از س...
A Novel Approach for Detecting Relationships in Social Networks Using Cellular Automata Based Graph Coloring
All the social networks can be modeled as a graph, where each roles as vertex and each relationroles as an edge. The graph can be show as G = [V;E], where V is the set of vertices and E is theset of edges. All social networks can be segmented to K groups, where there are members in eachgroup with same features. In each group each person knows other individuals and is in touch ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series C (Applied Statistics)
سال: 2013
ISSN: 0035-9254,1467-9876
DOI: 10.1111/rssc.12013