Bayesian logistic Gaussian process models for dynamic networks

Journal of Machine Learning Research

Volumn 33, Issue , 2014, Pages 194-201

  Durante, Daniele ^a   Dunson, David B ^b  

^a UNIVERSITY OF PADOVA   (Italy)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ENCODING (SYMBOLS); GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); SPORTS;

ADJACENCY MATRICES; BAYESIAN NONPARAMETRIC MODELING; GAUSSIAN PROCESS; GAUSSIAN PROCESS MODELS; LINK PROBABILITY; LOGISTIC MAPPING; PROBABILITY MATRIXES; RESEARCH FIELDS;

BAYESIAN NETWORKS;

EID: 84955514892     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (27)

1. E.M. Airoldi, D.M. Blei, S.E. Fienberg and E.P. Xing (2008). Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research 9:1981-2014.
2. A. Bhattacharya and D.B. Dunson (2011). Sparse Bayesian infinite factor models. Biometrika 98: 291-306.
3. K.A. Bollen (1989). Structural Equations with Latent Variables. New York: Wiley.
4. S. Chatterjee and P. Diaconis (2013). Estimating and understanding exponential random graph models. The Annals of Statistics 41: 2263-2702.
5. H.M. Choi and J.P. Hobert (2013). The Polya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic. Electronic Journal of Statistics 7: 2150-2163.
6. W.S. DeSarbo, Y. Kim and D. Fong (1998). A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data. Journal of Econometrics 89:79-108.
7. W.S. DeSarbo and D.L. Hoffman (1987). Constructing MDS joint spaces from binary choice data: A new multidimensional unfolding threshold model for marketing research. Journal of Marketing Research 24:40-54.
8. M.J. Dixon and S.G. Coles (1997). Modelling association football scores and inefficiencies in the football betting market. Applied Statistics 46:265-280.
9. D. Durante and D.B. Dunson (2013). Nonparametric Bayes dynamic modeling of relational data. arxiv.org/abs/1311.4669.
10. P. Erdös and A. and Rény (1959). On Random Graphs. Publicationes Mathematicae 6:290-297.
11. O. Frank and D. Strauss (1986). Markov Graphs. Journal of the American Statistical Association 81:832-842.
12. F. Guo, S. Hanneke, W. Fu and E.P. Xing (2007). Recovering temporally rewiring networks: A modelbased approach. In Proceedings of the International Conference in Machine Learning.
13. M.S. Handcock, G.L. Robins, T.A.B. Snijders, J. Moody and J. Besag (2003). Assessing Degeneracy in Statistical Models of Social Networks. Journal of the American Statistical Association 76:33-50.
14. C. Heaukulani and W. Ghahramani (2013). Dynamic probabilistic models for latent feature propagation in social networks. In Proceedings of the International Conference in Machine Learning.
15. P.D. Hoff, A.E. Raftery and M.S. Handcock (2002). Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association 97:1090-1098.
16. P.W. Holland and S. Leinhardt (1981). An Exponential Family of Probability Distributions for Directed Graphs. Journal of the American Statistical Association 76:62-65.
17. K. Ishiguro, T. Iwata, N. Ueda and J. Tenenbaum (2010). Dynamic infinite relational model for time-varying relational data analysis. In Advances in Neural Information Processing Systems.
18. C. Kemp, J.B. Tenenbaum, T.L. Griffiths, T. Yamada and N. Ueda (2006). Learning systems of concepts with an infinite relational model. In Proceedings of the National Conference on Artificial Intelligence.
19. K. Nowicki and T.A.B. Snijders (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96:1077-1087.
20. N.G. Polson, J.G. Scott and J. Windle (2013). Bayesian inference for logistic models using Polya-Gamma latent variables. Journal of the American Statistical Association (forthcoming).
21. G.L. Robins and P.E. Pattison (2001). Random graph models for temporal processes in social networks. Journal of Mathematical Sociology 25:5-41.
22. P. Sarkar, S.M. Siddiqi and G.J. Gordon (2007). A latent space approach to dynamic embedding of cooccurrence data. In Proceedings of the International Conference on Artificial Intelligence and Statistics.
23. P. Sarkar and A.W. Moore (2005). Dynamic social network analysis using latent space models. In Advances in Neural Information Processing Systems.
24. E.P. Xing, W. Fu, W. and L. Song (2010). A StateSpace Mixed Membership Blockmodel for Dynamic Network Tomography. The Annals of Applied Statistics 4:535-566.
25. L. Xiong, X. Chen, T.Z. Huang, J. Schneider and J.G. Carbonell (2010). Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. In proceedings of SIAM Data Mining.
26. S.K. Xu and A.O. Hero III (2013). Dynamic stochastic blockmodels: Statistical models for time-evolving networks. arxiv.org/abs/1304.5974.
27. B. Zhu and D.B. Dunson (2013). Locally adaptive Bayes nonparametric regression via nested Gaussian processes. Journal of the American Statistical Association 108:1445-1456.