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SUNBELT 2020 PARIS https://urldefense.proofpoint.com/v2/url?u=https-3A__www.insna.org_events_sunbelt-2D2020&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-6z8hS2-q2NM-Bb-iHK2oBKcV8cMJrekmiixcC6fvEU&s=aAkspLhB58SE9hhm2HOB5VIqVGgOtWanSO_rnFR1gZ0&e= 
**WORKSHOP ON BERGM**
Bayesian Exponential Random Graph Models with R

WHEN: 3 - 6pm, Tuesday, June 2, 2020
INSTRUCTOR: Alberto Caimo (https://urldefense.proofpoint.com/v2/url?u=http-3A__acaimo.github.io&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-6z8hS2-q2NM-Bb-iHK2oBKcV8cMJrekmiixcC6fvEU&s=vc0qlB-7vvjaoINF12NbiJzUqWZmOdIcXeRTfd7J2wA&e= ), TU Dublin, Ireland
BERGM WEBSITE: https://urldefense.proofpoint.com/v2/url?u=http-3A__acaimo.github.io_Bergm&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-6z8hS2-q2NM-Bb-iHK2oBKcV8cMJrekmiixcC6fvEU&s=izptb8iEZYROmb33KmmPVaf36lymiWzett_U_AZod0o&e= 
CRAN: https://urldefense.proofpoint.com/v2/url?u=https-3A__CRAN.R-2Dproject.org_package-3DBergm&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=-6z8hS2-q2NM-Bb-iHK2oBKcV8cMJrekmiixcC6fvEU&s=f9cFs2zX5evNzXnIE30zxwtiXlvtxhtFK9nf3I-4g08&e= 

DESCRIPTION:
Bayesian analysis is a promising approach to social network analysis
because it yields a rich fully probabilistic picture of uncertainty which
is essential when dealing with  relational data. Using a Bayesian framework
for exponential random graph models (ERGMs) leads directly to the inclusion
of prior information about the network effects and provides access to the
uncertainties by evaluating the posterior distribution of the parameters.
The growing interest in Bayesian ERGMs can be attributed to the development
of very efficient computational tools developed over the last decade (e.g.,
[1] and [2]).

This hands-on 3-hour workshop will provide participants with the
opportunity to acquire essential knowledge of the main characteristics of
Bayesian ERGMs using the latest version of the Bergm package for R [3].
The workshop will have a strong focus on the practical implementation of
the package functions through the analysis of real network data.
Interactive material will support the acquisition of concepts and
understanding of the tutorial through code, scripts, and documentation.

MAIN TOPICS:
- Introduction to the Bayesian ERGMs;
- Prior specification;
- Model selection and estimation;
- Interpretation of model and parameter posterior estimates;
- Missing data imputation;
- Model assessment via goodness-of-fit procedures.

PREREQUISITES: Basic knowledge of exponential random graph models and R.

REFERENCES
[1] Caimo, A. and Friel, N. (2011). Bayesian Inference for Exponential
Random Graph Models, Social Networks, 33(1), 41-55.

[2] Bouranis, L., Friel, N., and Maire, F. (2017). Efficient Bayesian
inference for exponential random graph models by correcting the
pseudo-posterior distribution. Social Networks, 50, 98-108.

[3] Caimo, A. and Friel, N. (2014). Bergm: Bayesian Exponential Random
Graphs in R, Journal of Statistical Software, 61(2), 1-25.
jstatsoft.org/v61/i02

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