... quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub. Bayesian decision theory hospital profiling quality assurance quality of care The evaluation of the quality of care that patients receive from hospitals or other healthcare providers has gained increasing...

... design Bayesian decision theory Prior-data conflict Robust prior Sampling-analysis prior Bayesian clinical trial designs are often evaluated in terms of frequentist (i.e., conditional on a given parameter value) operating characteristics such as type I error rate, power, and mean squared error (MSE...

... on the posterior risk depend on the specification of the underlying loss function and prior assumptions. Illustrations are given for normal linear regression models. bayesian decision theory influence posterior risks regression models REFERENCES 1 Belsley , D. A. , Kuh , E. and Welsch , R.E...

... of designing sampling inspection plans by attributes. In the later parts of the paper a similar approach is adopted to inspection by variables. sampling inspection by variables by attributes fisher-cornish expansions binomial approximation poisson approximation bayesian decision theory neutral boundary...

...: Bayesian decision theory; Imprecise probabilities; Intervals of measures; Lower and upper bounds for expected loss 1. Introduction The Bayesian theory of statistical inference demands a prior distribution for some parameter. The prior represents information available before experimental data become...

... life lengths and relate it to the approach specified in standard MIL STD 781C. Keywords: ACCEPTANCE SAMPLING; BAYESIAN DECISION THEORY; BIOASSAYS; GAME THEORY; MILITARY STANDARDS; PREPOSTERIOR ANALYSIS; QUALIFICATION TESTING; QUALITY CONTROL; RELIABILITY DEMONSTRATION 1. INTRODUCTION AND OVERVIEW...

.../standard_publication_model ) Abstract Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single...

... information Bayesian decision theory beef cattle genetics quality signaling random sampling sample size determination C10 Q10 The methods to determine sample size can be classified into two broad categories: frequentist and Bayesian ( Adcock 1997 ). The debate between proponents of these two approaches...

... institutions. Bayesian decision theory and economic surplus analysis have been suggested as possible approaches to evaluate that information. This article takes a critical look at the strengths and weaknesses of combining those approaches for empirical evaluation of agricultural economics research. It presents...

... as opposed to separate univariate regressions. It also emphasises that within a Bayesian framework more variables than observations can be utilised. Bayesian decision theory; Determinism; Multivariate regression; Near infrared spectroscopy; Non-conjugate distribution; Prediction; Quadratic loss; Simulated...

...R. J. BOYS; K. D. GLAZEBROOK © 1992 Biometrika Trust 1992 Abstract SUMMARY Optimal screening procedures are derived using Bayesian decision theory. A simple heuristic solution is determined using an asymptotic posterior distribution for the parameters in a linear probit model. The solution...

... Institute of Science and Technology SUMMARY Setting the problem of approximating an underlying nonlinear time series model within the framework of Bayesian decision theory, we demonstrate how the general analysis of discontinuous decision processes developed by Smith...

... techniques are derived as special limiting cases. The results of the procedure applied to two data sets are compared with other analyses. Some key words: Bayesian decision theory; Cluster analysis; Exponential family; Similarity matrix; Unknown number of groups; Variable metrics...

..., University College London SlJMMABY A Bayesian decision theory approach to the choice of regression design is considered when it is intended to use the regression to help control the dependent variable at a chosen value, the control to be effected by choosing certain...

... reanalyzed using the Bayesian linear model framework of Lindley & Smith (1972), and a wider class of serial sampling acceptance schemes derived and compared. Autoregressive prior distribution Bayesian decision theory Bayesian linear model Matrix inversion Sampling acceptance schemes Biometrika (1976...

... are carried out for n= 15. Some key words: Bayesian decision theory; Dynamic programming; Maximum likelihood estimation; Optimal stopping; Sequential sampling; Urn models. 1. INTRODUCTION Suppose that an urn contains a known number of balls, an unknown number r...

... the best subset of independent variables to use for the prediction determined. Linear regression Optimal design Choice of variables Prediction Bayesian decision theory Biometrika (1972), 59, 3, p. 663 553 With 1 text-figure * Printed in Qreat Britain A decision theory approach to optimal...

... f₁ f₂ Wager against God –∞ f₃ Pascal presents at least three ‘wagers’ for believing in God. Hacking provides three reconstructions of them using the apparatus of Bayesian decision theory — dominance reasoning and calculations of expected...

...—a formalization of decision-making problems in which actors have the choice of whether and how to gather information to improve what happens. The chapter notes that, according to Bayesian decision theory, it is often optimal for active observers to remain incorrectly calibrated with their surroundings...

... capacity Bayesian decision theory cognitive maps perception language of thought A venerable tradition holds that the mind is stocked with mental representations: mental items that represent. There is a mental representation whale that represents whales, a mental representation...