Results 1  10
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18
Robustly detecting differential expression in rna sequencing data using observation weights. Nucleic acids research
, 2014
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Extending INLA to a class of nearGaussian latent models
 Department of Mathematical Sciences
, 2012
"... This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a nearGaussian distribution. The proposed methodology is an essential component of a bigger project tha ..."
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Cited by 5 (3 self)
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This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a nearGaussian distribution. The proposed methodology is an essential component of a bigger project that aim to extend the R package INLA (RINLA) in order to allow the user to add flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward and intuitive way. Our approach is applied to two examples and the results are compared with that obtained by Markov Chain Monte Carlo (MCMC), showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the RINLA package.
Bayesian Analysis of Measurement Error Models Using INLA. Available online: http://arxiv.org/pdf/1302.3065v2.pdf (accessed on 31
, 2013
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Penalising model component complexity: A principled, practical approach to constructing priors. submitted
, 2015
"... The issue of setting prior distributions on model parameters, or to attribute uncertainty for model parameters, is a difficult issue in applied Bayesian statistics. Although the prior distribution should ideally encode the users ’ prior knowledge about the parameters, this level of knowledge transfe ..."
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Cited by 2 (0 self)
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The issue of setting prior distributions on model parameters, or to attribute uncertainty for model parameters, is a difficult issue in applied Bayesian statistics. Although the prior distribution should ideally encode the users ’ prior knowledge about the parameters, this level of knowledge transfer seems to be unattainable in practice and applied statisticians are forced to search for a “default ” prior. Despite the development of objective priors, which are only available explicitly for a small number of highly restricted model classes, the applied statistician has few practical guidelines to follow when choosing the priors. An easy way out of this dilemma is to reuse prior choices of others, with an appropriate reference. In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a userdefined scaling parameter for that model component. These priors are invariant to reparameterisations, have a natural connection to Jeffreys ’ priors, are designed to support Occam’s razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions.
Twostage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions
"... Abstract Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalizatio ..."
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Abstract Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalization or mortality data. However this approach limits the analysis to individuals characterized by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population. We propose a twostage statistical approach: in the first stage we specify a spacetime model to estimate the monthly NO 2 concentration integrating several data sources characterized by different spatiotemporal resolution; in the second stage we link the concentration to the β 2 agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.
Approximation of Bayesian predictive pvalues with regression ABC. [Working Paper]
"... This is the author’s version of a work that was submitted/accepted for pub ..."
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This is the author’s version of a work that was submitted/accepted for pub
Bayesian Computational Tools∗
"... Abstract: This chapter surveys advances in the field of Bayesian computation over the past twenty years, from a purely personnal viewpoint, hence containing some ommissions given the spectrum of the field. Monte Carlo, MCMC and ABC themes are thus covered here, while the rapidly expanding area of p ..."
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Abstract: This chapter surveys advances in the field of Bayesian computation over the past twenty years, from a purely personnal viewpoint, hence containing some ommissions given the spectrum of the field. Monte Carlo, MCMC and ABC themes are thus covered here, while the rapidly expanding area of particle methods is only briefly mentioned and different approximative techniques like variational Bayes and linear Bayes methods do not appear at all. This chapter also contains some novel computational entries on the doubleexponential model that may be of interest per se.
Distributed under Creative Commons CCBY 4.0 OPEN ACCESS
, 2015
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