2.1 Basic setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Gibbs formalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4 ...

We establish bounds for the measure of deviation sets associated to continuous observables with respect to weak Gibbs measures. Under some mild assumptions, we obtain upper and lower bounds for the measure of deviation sets of some non-uniformly expanding maps, including quadratic maps and robust multidimensional non-uniformly expanding local diffeomorphisms.

Motivation: Genes code for proteins, the action molecules of life. The bulk of scientific effort has focused on the genes and their products. Yet only about 3% of the human genome codes for genes. The remainder, sometimes called "junk DNA", has received far less attention. Recent findings suggest that this junk may be far more important than previously believed. In particular this "junk" has be...

We describe a computer program to assist a clinician with assessing the e cacy of treatments in experimental studies for which treatment assignment is random but subject compliance is imperfect. The major di culty in such studies is that treatment e cacy is not \identi able", that is, it cannot be estimated from the data, even when the number of subjects is in nite, unless additional knowledge ...

This study was undertaken to estimate the genetic parameters for some reproduction traits in Sistani beef cattle. The data set consisted of 1489 records of number of insemination, calving, and insemination dates in different calving was used. Reproduction traits including calving interval (CI), gestation length (GL), days open (DO), calving to first service (CTFS), first service to conception (...

Gibbs sampling, also known as Coordinate Hit-and-Run (CHAR), is a Markov chain Monte Carlo algorithm for sampling from high-dimensional distributions. In each step, the selects random coordinate and re-samples that distribution induced by fixing all other coordinates. While this has become widely used over past half-century, guarantees of efficient convergence have been elusive. We show convex ...