Haut PDF High dimensional Bayesian computation
High dimensional Bayesian computation
... Computational Bayesian statistics builds approximations of the posterior distribution either by sampling or by constructing tractable ...of Bayesian stastics is the development of new methodology by ...
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Incremental bayesian network structure learning in high dimensional domains
... for Bayesian network structure learning. It could deal with high dimensional domains, where whole dataset is not completely available, but grows ...
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CrossCat: A fully Bayesian nonparametric method for analyzing heterogeneous, high dimensional data
... analyze high-dimensional datasets with- out imposing restrictive or opaque modeling ...approximately Bayesian inference in a hierarchical, nonparamet- ric model for data ...and Bayesian net- ...
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Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression
... 1 Introduction Over the past decades, parsimony has emerged as a very natural way to deal with high- dimensional data spaces (Cand` es, 2014). In the context of linear regression, finding a parsimonious ...
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Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity
... the high performances for the classification and the great stability for the variable ...with high-dimensional data and for which an interpretation of the model is expected such as in all ...
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Convergence et spike and Slab Bayesian posterior distributions in some high dimensional models
... Abstract Title : Convergence of Spike and Slab Bayesian posterior distributions in some high dimensional models. The first main focus is the sparse Gaussian sequence model. An Empirical Bayes ...
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High-dimensional dependence modelling using Bayesian networks for the degradation of civil infrastructures and other applications
... investigated high-dimension deterioration problems using Bayesian ...of high-dimensional ...that Bayesian networks can be a versatile framework in which both statistical and ...
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Mixture of markov trees for bayesian network structure learning with small datasets in high dimensional space
... 2 Bayesian network structure learning in high dimension ...Introduction Bayesian network structure learning is NP-hard and existing algorithms are not scalable to very high dimensional ...
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High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks
... of Bayesian networks aims at model- ing the joint density of a set of random variables from a random sample of joint observations of these variables (Cowell et ...for Bayesian network structure learning are ...
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Uncertainty quantification on pareto fronts and high-dimensional strategies in bayesian optimization, with applications in multi-objective automotive design
... in high-dimension [ GBC + 14 ...fully Bayesian approach, but then the predictive distribution has no more closed form expression, thus re- quiring the use of more computationally demanding techniques based ...
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High-Dimensional Bayesian Multi-Objective Optimization
... Chapter 1 Introduction As is common in design engineering, a vehicle is made of several systems interacting together, such as the engine, the suspensions, the bodystructure, electrical devices. To guarantee performance, ...
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High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
... R d e −U (y) dy. Such problem naturally occurs for example in Bayesian inference and machine learning. Under the assumption that U is continuously differentiable, ∇U is globally Lipschitz and U is strongly convex, ...
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Amount of information needed for model choice in Approximate Bayesian Computation
... Additional signals in the third and fourth PCs indicate that derived alleles describe some of the variation, even if these PCs accounted for a small fraction of the overall variation (about 1.7% combined). Combining the ...
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Approximation of high-dimensional parametric PDEs
... infinite dimensional framework, and not covered in our paper, let us mention the following related works: (i) similar holomorphy and approximation results are established in [47, 48, 58] for specific type of PDEs ...
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Bayesian computation: a perspective on the current state, and sampling backwards and forwards
... To address the second difficulty with adaptive al- gorithms, several approaches have been developed to establish their theoretical underpinning. While for stan- dard MCMC convergence in total variation and law of large ...
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HIV with contact-tracing: a case study in Approximate Bayesian Computation
... In this paper, we consider both finite and infinite dimensional summary statistics for ABC. When comparing ABC with the two different sets of statistics, we find that the point estimates of the parameters λ 1 , λ ...
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Approximation of high-dimensional parametric PDEs
... infinite dimensional framework, and not covered in our paper, let us mention the following related works: (i) similar holomorphy and approximation results are established in [47, 48, 58] for specific type of PDEs ...
148
Bayesian multi-locus pattern selection and computation through reversible jump MCMC
... we have designed an innovative approach. Within this framework, the relationship between the genetic variants and the quantitative phenotype is modeled through a multivariate linear model. Then, to only focus on parts ...
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Anderson Localization in high dimensional lattices
... level statistics are Poisson-like [ 129 ] and that the eigenfunctions are exponentially localized with an upper bound on the localization length that diverges at the presumed transition point [ 130 ]. About the ...
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Acceleration Strategies of Markov Chain Monte Carlo for Bayesian Computation
... MCMC samplers are based on two original versions: the Bouncy Particle Sampler (BPS) of Bouchard-Cˆ ot´ e et al. (2018) and and the Zigzag Sampler of Bierkens et al. (2016). Bouchard-Cˆ ot´ e et al. (2018) exhibits that ...
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