@article{Koblents2014b,

title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},

doi = {10.1007/s11222-013-9440-2},

issn = {0960-3174},

year  = {2015},

date = {2015-03-01},

journal = {Statistics and Computing},

volume = {25},

number = {2},

pages = {407--425},

abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},

keywords = {COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models},

pubstate = {published},

tppubtype = {article}

}

@article{Read2013b,

title = {Multi-Dimensional Classification with Super-Classes},

author = {Jesse Read and Concha Bielza and Pedro Larranaga},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6648319},

issn = {1041-4347},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {PP},

number = {99},

pages = {1--1},

abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},

keywords = {COMPREHENSION},

pubstate = {published},

tppubtype = {article}

}