• Bayesian inference for dynamic...

Result: Bayesian inference for dynamic social network data

Title:
Bayesian inference for dynamic social network data
Authors:
KOSKINEN, Johan H, SNIJDERS, Tom A. B
Source:
5th St. Petersburg workshop on simulation, St. Petersburg State University, St. Petersburg, Russia, 26 June-2 July 2005. Part IIJournal of statistical planning and inference. 137(12):3930-3938
Publisher Information:
Amsterdam; Lausanne; New York,NY: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Processus de markov, Markov processes, Statistiques, Statistics, Généralités, General topics, Inférence paramétrique, Parametric inference, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Arête graphe, Edge(graph), Arista gráfico, Chaîne Markov, Markov chain, Cadena Markov, Donnée observation, Observation data, Dato observación, Décision statistique, Statistical decision, Decisión estadística, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation statistique, Statistical estimation, Estimación estadística, Fonction répartition, Distribution function, Función distribución, Implémentation, Implementation, Implementación, Interaction, Interacción, Loi a posteriori, Posterior distribution, Ley a posteriori, Loi a priori, Prior distribution, Ley a priori, Loi probabilité, Probability distribution, Ley probabilidad, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Point fixe, Fix point, Punto fijo, Probabilité transition, Transition probability, Probabilidad transición, Temps continu, Continuous time, Tiempo continuo, 37C25, 60E05, 60J10, 62F15, 65C40, Estimation paramétrique, 91D30(62F15,62M05), Longitudinal social networks; Data augmentation; Bayesian inference; Random graphs
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, Stockholm University, Stockholm, Sweden
Department of Psychology University of Melbourne, Parkville VIC 3010, Australia
University of Oxford, Nuffield College, New Road, Oxford OX1 INF, United Kingdom
Department of Sociology, University of Groningen, Netherlands
ISSN:
0378-3758
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19061100
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.19061100
Database:
PASCAL Archive

Further Information

We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations.