Previous articleNext article No AccessThe PC Algorithm and the Inference to ConstitutionLorenzo Casini Michael BaumgartnerLorenzo Search for more articles by this author Baumgartner PDFPDF PLUS Add favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited The British Journal Philosophy of Science Just Accept...

Frequentist and likelihood based methods of inference encounter several difficulties with the multivariate skew-normal model. In spite of the popularity of this class of densities, there are no broadly satisfactory solutions for estimation and testing problems. In this paper we propose a general population Monte Carlo algorithm which exploits the stochastic representation of the skew-normal ran...

Microclustering refers to clustering models that produce small clusters or, equivalently, to models where the size of the clusters grows sublinearly with the number of samples. We formulate probabilistic microclustering models by assigning a prior distribution on the size of the clusters, and in particular consider microclustering models with explicit bounds on the size of the clusters. The com...

The main goal of this work is to introduce theoretical background of the extended forward inference algorithm. Proposed algorithm allow to continue inference after its failure. Inference failure means that the inference engine is unable to obtain the solutions — the new facts or goals confirmation. Two-phase extension of classical inference algorithm is considered. In the first phase, classical...

Indoor location services have become an increasingly important part of our everyday lives in recent years. Despite the numerous benefits these offer, serious concerns arisen about privacy users’ locations. Adversaries can monitor user-requested locations order to obtain sensitive information such as shopping patterns. Many users indoor spaces want their movements and be kept private so not reve...

We develop a Bayesian “sum-of-trees” model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting and inference are accomplished via an iterative backfitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting, each weak learner (i.e., each weak tree) contributes a small amount to the overall ...