Search results

Multimodal, high-dimension posterior distributions are well known to cause mixing problems for standard Markov chain Monte Carlo (MCMC) procedures; unfortunately such functional forms readily occur in empirical political science. This is a particularly important problem in applied Bayesian work because inferences are made from finite intervals of the Markov chain path. To address this issue, we develop and apply a new MCMC algorithm based on tempered transitions of simulated annealing, adding a dynamic element that allows the chain to self-tune its annealing schedule in response to current posterior features. This important feature prevents the Markov chain from getting trapped in minor modal areas for long periods of time. The algorithm is applied to a probabilistic spatial model of voting in which the objective function of interest is the candidate's expected return. We first show that such models can lead to complex target forms and then demonstrate that the dynamic algorithm easily handles even large problems of this kind.

The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species. The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used. This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture. Rongling Wu is Associate Professor of Statistics at the University of Florida, Gainesville. He currently serves as Associate Editor for six genetics and bioinformatics journals. Chang-Xing Ma is Assistant Professor of Biostatistics at the State University of New York at Buffalo. George Casella is Distinguished Professor of Statistics and Distinguished Member of the Genetics Institute at the University of Florida, Gainesville. He is a fellow of the American Statistical Association and the Institute of Mathematical Sciences, and the author of four other statistics books.