An empirical Bayes procedure for the selection of Gaussian graphical models

A new methodology for model determination in decomposable graphical Gaussian models is developed. The Bayesian paradigm is used and, for each given graph, a hyper inverse Wishart prior distribution on the covariance matrix is considered. This prior distribution depends on hyper-parameters. It is well-known that the models's posterior distribution is sensitive to the specification of these hyper-parameters and no completely satisfactory method is registered. In order to avoid this problem, we suggest adopting an empirical Bayes strategy, that is a strategy for which the values of the hyper-parameters are determined using the data. Typically, the hyper-parameters are fixed to their maximum likelihood estimations. In order to calculate these maximum likelihood estimations, we suggest a Markov chain Monte Carlo version of the Stochastic Approximation EM algorithm. Moreover, we introduce a new sampling scheme in the space of graphs that improves the add and delete proposal of Armstrong et al. (2009). We illustrate the efficiency of this new scheme on simulated and real datasets

Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures

In this paper we provide general conditions to check on the model and the prior to derive posterior concentration rates for data-dependent priors (or empirical Bayes approaches). We aim at providing conditions that are close to the conditions provided in the seminal paper by Ghosal and van der Vaart (2007a). We then apply the general theorem to two different settings: the estimation of a density using Dirichlet process mixtures of Gaussian random variables with base measure depending on some empirical quantities and the estimation of the intensity of a counting process under the Aalen model. A simulation study for inhomogeneous Poisson processes also illustrates our results. In the former case we also derive some results on the estimation of the mixing density and on the deconvolution problem. In the latter, we provide a general theorem on posterior concentration rates for counting processes with Aalen multiplicative intensity with priors not depending on the data.Comment: With supplementary materia

Incentives for BGP Guided IP-Level Topology Discovery

peer reviewedInternet topology discovery has been an attractive research field during the past decade. In particular, the research community was interested in modeling the network as well as providing efficient tools, mostly based on traceroute, for collecting data. In this paper, we follow this track of rendering traceroute-based exploration more efficient. We discuss incentives for coupling passive monitoring and active measurements. In particular, we show that high-level information, such as BGP updates, might be used to trigger targeted traceroutes. As a result, the network dynamics might be better capture. We also provide a freely available tool for listening to BGP feeds and triggering dedicated traceroutes

The Specialty Coffee Quality Rating as a Measure of Product Differentiation and Price Signal to Growers: an Entropy Analysis of E-Auction Data

Demand and Price Analysis,

Price Determinants in Top Quality E-Auctioned Specialty Coffees

The US specialty coffee industry has grown from $1 billion in 1990 to$11 billion in 2006 and is expected to continue to grow into the foreseeable future. This growth particularly depends on prices coordinating the specialty coffee supply chain through two-way information exchange between roasters and producers. We analyze the determinants of specialty coffee prices by estimating a hedonic price function for specialty Central and South American coffees traded at e-auctions. We hypothesize that since specialty coffee is a differentiated product, prices will be determined by both sensory and reputation attributes. The results show that prices are influenced by the quality rating, which is a sensory variable, and by the quality rankings established in the cupping competition previous to the auction, the country of origin and the coffee variety, which are reputation variables. In addition, the macro variables, harvest year and commodity price were found to be significant.Specialty coffee, hedonic price analysis, differentiated food pricing, sensory attributes, reputation attributes, Demand and Price Analysis,