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pro vyhledávání: '"Caimo, A"'
Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the compl
Externí odkaz:
http://arxiv.org/abs/2104.02444
Publikováno v:
In Environmental Science and Policy October 2023 148
Autor:
Caimo, Alberto, Gollini, Isabella
A new modelling approach for the analysis of weighted networks with ordinal/polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random g
Externí odkaz:
http://arxiv.org/abs/1811.07025
Publikováno v:
Journal of Statistical Software, Vol 104, Pp 1-23 (2022)
Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the compl
Externí odkaz:
https://doaj.org/article/9423e8e5545d484195811562a3f01882
Publikováno v:
In Safety Science October 2022 154
Autor:
Caimo, Alberto, Friel, Nial
The Bergm package provides a comprehensive framework for Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy. The package is si
Externí odkaz:
http://arxiv.org/abs/1703.05144
Autor:
Caimo, Alberto, Gollini, Isabella
In this chapter we review some of the most recent computational advances in the rapidly expanding field of statistical social network analysis using the R open-source software. In particular we will focus on Bayesian estimation for two important fami
Externí odkaz:
http://arxiv.org/abs/1504.03152
Publikováno v:
In Social Networks October 2020 63:134-149
Autor:
Caimo, Alberto, Gollini, Isabella
Publikováno v:
In Computational Statistics and Data Analysis February 2020 142
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the
Externí odkaz:
http://arxiv.org/abs/1407.6895