Incomplete graphical model inference via latent tree aggregation

Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expression or species abundances for instance. In many practical cases, not all variables involved in the network have been observed, and the samples are actually drawn from a distribution where some variables have been marginalized out. This challenges the sparsity assumption commonly made in graphical model inference, since marginalization yields locally dense structures, even when the original network is sparse. We present a procedure for inferring Gaussian graphical models when some variables are unobserved, that accounts both for the influence of missing variables and the low density of the original network. Our model is based on the aggregation of spanning trees, and the estimation procedure on the Expectation-Maximization algorithm. We treat the graph structure and the unobserved nodes as missing variables and compute posterior probabilities of edge appearance. To provide a complete methodology, we also propose several model selection criteria to estimate the number of missing nodes. A simulation study and an illustration flow cytometry data reveal that our method has favorable edge detection properties compared to existing graph inference techniques. The methods are implemented in an R package

Competitive tendering in the Scottish National Health Service Was it compulsory, and did it make a difference?(*)

This paper examines the implementation of competitive tendering in the Scottish National Health Service. Data relating to cleaning, catering and laundering services-- the three services targeted for competitive tendering--are examined. Our analysis suggests that for the first four years the request to market test was largely ignored in Scotland. In 1987 it become a management requirement, and within three years of its fresh start implementation of this policy more than matched the corresponding experience in England.

The promise of Gaia and how it will influence stellar ages

The Gaia space project, planned for launch in 2011, is one of the ESA cornerstone missions, and will provide astrometric, photometric and spectroscopic data of very high quality for about one billion stars brighter than V=20. This will allow to reach an unprecedented level of information and knowledge on several of the most fundamental astrophysical issues, such as mapping of the Milky Way, stellar physics (classification and parameterization), Galactic kinematics and dynamics, study of the resolved stellar populations in the Local Group, distance scale and age of the Universe, dark matter distribution (potential tracers), reference frame (quasars, astrometry), planet detection, fundamental physics, Solar physics, Solar system science. I will present a description of the instrument and its main characteristics, and discuss a few specific science cases where Gaia data promise to contribute fundamental improvement within the scope of this Symposium.Comment: 10 pages, 2 figures, IAU Symp. 258 on "The Ages of Stars

Designing a greener product: the Hoover 'New Wave' washing machine range

Hoover is one of the best known manufacturers of domestic appliances in Britain and has made washing machines at its Merthyr Tydfil factory since 1948. This article discusses the creation of the Hoover New Wave range of ‘green’ washing machines, which were launched in 1993 after a four year programme of research, design and development and investment in new manufacturing plant costing £15 million.The New Wave range were the first products to be awarded an EU Ecolabel, having exceeded set criteria for energy, water and detergent consumption and wash performance