@article{Ruiz2014,

title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},

author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},

url = {http://jmlr.org/papers/volume15/ruiz14a/ruiz14a.pdf 

http://arxiv.org/abs/1401.7620},

year  = {2014},

date = {2014-01-01},

journal = {Journal of Machine Learning Research},

volume = {15},

number = {1},

pages = {1215--1248},

abstract = {The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first 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 integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.},

keywords = {ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference},

pubstate = {published},

tppubtype = {article}

}

@article{TausteCampo2014,

title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {60},

number = {6},

pages = {3209--3217},

publisher = {IEEE},

abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},

keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},

pubstate = {published},

tppubtype = {article}

}

@article{GilTaborda2014,

title = {Information--Estimation Relationships over Binomial and Negative Binomial Models},

author = {Camilo G Taborda and Dongning Guo and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6746122},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {to appear},

pages = {1--1},

publisher = {IEEE},

abstract = {In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {article}

}