Physical Sciences and Mathematics Commons^™

A Bayesian Approach To Deriving Ages Of Individual Field White Dwarfs, Erin M. O'Malley, Ted Von Hippel, David A. Van Dyk

Ted von Hippel

We apply a self-consistent and robust Bayesian statistical approach to determine the ages, distances, and zero-age main sequence (ZAMS) masses of 28 field DA white dwarfs (WDs) with ages of approximately 4-8 Gyr. Our technique requires only quality optical and near-infrared photometry to derive ages with <15% uncertainties, generally with little sensitivity to our choice of modern initial-final mass relation. We find that age, distance, and ZAMS mass are correlated in a manner that is too complex to be captured by traditional error propagation techniques. We further find that the posterior distributions of age are often asymmetric, indicating that the standard approach to deriving WD ages can yield misleading results.

Differentially Expressed Genes In Blood From Young Pigs Between Two Swine Lines Divergently Selected For Feed Efficiency: Potential Biomarkers For Improving Feed Efficiency, Haibo Liu, Yet T. Nguyen, Daniel S. Nettleton, Jack C. M. Dekkers, Christopher K. Tuggle

Dan Nettleton

The goal of this study was to find potential gene expression biomarkers in blood of piglets that can be used to predict pigs’ future feed efficiency. Using RNA-seq technology, we found 453 genes were differentially expressed (false discovery rate (FDR) ≤ 0.05) in the blood of two Yorkshire lines of pigs divergently selected for feed efficiency (FE) based on residual feed intake (RFI). Genes involved in several biosynthetic processes were overrepresented among genes more highly expressed in the low RFI line compared to the high RFI line. Weighted gene co-expression network analysis (WGCNA) also revealed genes involved in some of …

A Self-Contained Course In The Mathematical Theory Of Statistics For Scientists & Engineers With An Emphasis On Predictive Regression Modeling & Financial Applications., Tim Smith

Timothy Smith

Preface & Acknowledgments

This textbook is designed for a higher level undergraduate, perhaps even first year graduate, course for engineering or science students who are interested to gain knowledge of using data analysis to make predictive models. While there is no statistical perquisite knowledge required to read this book, due to the fact that the study is designed for the reader to truly understand the underlying theory rather than just learn how to read computer output, it would be best read with some familiarity of elementary statistics. The book is self-contained and the only true perquisite knowledge is a solid …