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Bayesian Nonparametric Modeling of Suicide Attempts
Francisco Ruiz · Isabel Valera · Carlos Blanco · Fernando Perez-Cruz

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database contains a large amount of information, regarding the way of life, medical conditions, depression, etc., of a representative sample of the U.S. population. In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the nature of the data, we need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows us to integrate out the weighting factors of the multinomial-logit likelihood model. Finally, the experiments over the NESARC database show that our model properly captures some of the hidden causes that model suicide attempts.

Author Information

Francisco J. R. Ruiz (Columbia University)
Isabel Valera (UC3M)
Carlos Blanco (Columbia University College of Physicians and Surgeons)
Fernando Perez-Cruz (Swiss Data Science Center (ETH Zurich and EPFL))

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