Scalable Bayesian modelling for smoothing disease risks in large spatial data sets using INLA

Bayesian Spatial Modelling with R-INLA

The principles behind the interface to continuous domain spatial models in the RINLA software package for R are described. The Integrated Nested Laplace Approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging f...

Scalable Anonymization Algorithms for Large Data Sets

k-Anonymity is a widely-studied mechanism for protecting identity when distributing non-aggregate personal data. This basic mechanism can also be extended to protect an individual-level sensitive attribute. Numerous algorithms have been developed in recent years for generalizing, clustering, or otherwise manipulating data to satisfy one or more anonymity requirements. However, few have consider...

Bayesian Prediction in Spatial Data Analysis

Approximating likelihoods for large spatial data sets

Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O.n3/ operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is ...

A Novel Hyperbolic Smoothing Algorithm for Clustering Large Data Sets

The minimum sum-of-squares clustering problem is considered. The mathematical modeling of this problem leads to a min − sum −min formulation which, in addition to its intrinsic bi-level nature, has the significant characteristic of being strongly nondifferentiable. To overcome these difficulties, the proposed resolution method, called Hyperbolic Smoothing, adopts a smoothing strategy using a sp...