Physical Sciences and Mathematics Commons^™

Combinatorial Stochastic Processes , Jim Pitman

Jim Pitman

This is a set of lecture notes for a course given at the St. Flour summer school in July 2002. The theme of the course is the study of various combinatorial models of random partitions and random trees, and the asymptotics of these models related to continuous parameter stochastic processes. Following is a list of the main topics treated: models for random combinatorial structures, such as trees, forests, permutations, mappings, and partitions; probabilistic interpretations of various combinatorial notions e.g. Bell polynomials, Stirling numbers, polynomials of binomial type, Lagrange inversion; Kingman's theory of exchangeable random partitions and random discrete distributions; connections …

Water Use Benchmarks For Thermoelectric Power Generation, Stanley Mubako

Stanley Mubako

No abstract provided.

Synthesis And Spectral Studies Of A Novel 20-Membered Unsymmetrical Dinucleating [N8] Macrocycle And Its Bimetallic Complexes, M2lcln(Clo4)2 (N = 2, M = Co, Ni Or Cu; N = 4, M = Cr Or Fe), Mohammad Mansoob Khan Dr, Z. A. Siddiqi Dr

Dr. Mohammad Mansoob Khan

A 20-membered octaaza unsymmetrical Schiff base macrocycle, 6,10,16,20-tetramethyl-7,9,17,19-tetraphenyl [1,5,7,9,11,15,17,19]octaazacycloeicosa-5,10,15,20-tetraene dihydroperchlorate, has been obtained as an...

Controlled Generation Of Hard And Easy Bayesian Networks: Impact On Maximal Clique Size In Tree Clustering, Ole J. Mengshoel, David C. Wilkins, Dan Roth

Ole J Mengshoel

This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. The results are relevant to research on efficient Bayesian network inference, such as computing a most probable explanation or belief updating, since they allow controlled experimentation to determine the impact of improvements to inference algorithms. The results are also relevant to research on machine learning of Bayesian networks, since they support controlled generation of a large number of data sets at a given difficulty level. Our generation algorithms, called BPART and MPART, support controlled but random construction …

Exchange Coupling And Contribution Of Induced Orbital Angular Momentum Of Low-Spin Fe^3+ Ions To Magnetic Anisotropy In Cyanide-Bridged Fe2m2 Molecular Magnets: Spin-Polarized Density-Functional Calculations, Stephen Holmes, Kyungwha Park

Stephen Holmes

Environmental Education In Action For The Future, Sandra Wooltorton, Dora Marinova

Sandra Wooltorton

The Worsley Energy Challenge To Reduce Energy Consumption: Report On The Project Start-Up, Sandra Wooltorton, Richard Jeffreys

Sandra Wooltorton

Regression With Signals And Images As Predictors, Philip T. Reiss

Philip T. Reiss

Signal regression and image regression, in which the outcomes are scalars and the predictors are one-dimensional signals or multidimensional images, are of interest in many scientific fields. The principal statistical challenge is how to reduce the dimension of the predictors in what would otherwise be a severely ill-posed problem. A pair of novel methods, functional principal component regression (FPCR) and functional partial least squares (FPLS), combine two existing approaches to the dimension reduction problem: selection of most relevant components, as is done in ordinary principal component regression (PCR) and partial least squares (PLS), and restriction of the coefficient function to …

1,4,10,13-Tetra­Oxa-7,16-Diazo­Niacyclo­Octa­Decane Bis­(Hexa­Fluoro­Phosphate): Pf6− Acting As A Pseudohalide, Daron E. Janzen, Maikel E. Botros, Gregory P. Helton, Donald G. Vanderveer, Gregory J. Grant

Daron E Janzen, Ph.D.

No abstract provided.

Progress Towards The Stereoselective Synthesis Of Korupensamine A, Andrew A. Lover

Andrew Lover