When using graphical models for decision making, a fundamental question is whether one is ready to make a decision (stopping criteria), and if not, what observations should be made to better prepare for a decision (selection criteria). In this paper, we review the notions of entropy and expected utility, which are commonly used for this purpose, and contrast them with a newly introduced notion,...

Numerous engineering systems gradually deteriorate due to internal stress caused by the working load. The system deterioration process is directly related workload, providing opportunities for decision-makers manage modifying workload. As one of most effective ways control malfunction risk, mission stopping has been extensively studied. Most existing research on ignores effect loads safety-crit...

BACKGROUND & AIMS Isoniazid is a leading cause of liver injury but it is not clear how many cases are reported or how many clinicians and patients adhere to American Thoracic Society (ATS) guidelines. We collected data on cases of isoniazid hepatotoxicity and assessed adherence to ATS guidelines and reports to the Centers for Disease Control's (CDC) isoniazid severe adverse events program. ME...

MOTIVATION The standard paradigm for a classifier design is to obtain a sample of feature-label pairs and then to apply a classification rule to derive a classifier from the sample data. Typically in laboratory situations the sample size is limited by cost, time or availability of sample material. Thus, an investigator may wish to consider a sequential approach in which there is a sufficient nu...

Stopping criteria for Stochastic Gradient Descent (SGD) methods play important roles from enabling adaptive step size schemes to providing rigor downstream analyses such as asymptotic inference. Unfortunately, current stopping SGD are often heuristics that rely on normality results or convergence stationary distributions, which may fail exist nonconvex functions and, thereby, limit the applicab...

Phylogenetic bootstrapping (BS) is a standard technique for inferring confidence values on phylogenetic trees that is based on reconstructing many trees from minor variations of the input data, trees called replicates. BS is used with all phylogenetic reconstruction approaches, but we focus here on one of the most popular, maximum likelihood (ML). Because ML inference is so computationally dema...