background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

This paper discusses the use of various scoring metrics in the Bayesian optimization algorithm BOA which uses Bayesian networks to model promising solutions and generate the new ones The use of decision graphs in Bayesian networks to improve the performance of the BOA is proposed To favor simple models a complexity measure is incorporated into the Bayesian Dirichlet metric for Bayesian networks...

In this paper we introduce a stochastic optimization method based ona mixed Bayesian/frequentist approach to a sample size determinationproblem in a clinical trial. The data are assumed to come from a nor-mal distribution for which both the mean and the variance are unknown.In contrast to the usual Bayesian decision theoretic methodology, whichassumes a single decision maker, our method recogni...

Economic evaluation of health care interventions based on decision analytic modelling can generate valuable information for health policy decision makers. However, the usefulness of the results obtained depends on the quality of the data input into the model; that is, the accuracy of the estimates for the costs, effectiveness, and transition probabilities between the different health states of ...

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-tria...

Traditionally Bayesian decision-theoretic design of experiments proceeds by choosing a to minimise expectation given loss function over the space all designs. The encapsulates aim experiment, and is taken with respect joint distribution unknown quantities implied statistical model that will be fitted observed responses. In this paper, an extended framework proposed whereby alternative model. Mo...

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on h...

Improving venture capitalists’ decision processes is key to reducing failure rates for venture capital backed companies and to improving portfolio returns. In this paper we describe the use of a novel technique—Bayesian causal maps—to support and improve venture capital decision making. We combine causal mapping and Bayesian network techniques to construct a Bayesian causal map. The resulting p...