A standard Bayesian stopping rule for sequential trials is based on the posterior probability that a treatment effect exceeds a minimum relevant clinical threshold. In this paper we consider a robust version of this criterion by replacing the single prior distribution with a class of prior distributions. We compare the average sample sizes of the robust sequential approach both with the sample ...

Subjects display sensitivity to local patterns in stimulus history (sequential effects) in a variety of decision-making, perceptual, and motor tasks. Sequential effects are typically cast as a prime example of human irrationality. We propose a Bayesian model that explains sequential effects as the natural consequence of one incorrect assumption: instead of assuming a stable world, subjects assu...

Bayesian model or Beta-binomial conjugate using Bayesian sequential estimation method to estimate the proportion of different age groups is compared with the conventional multivariate control chart method. The parameters for the techniques were derived and applied. The result shows that the patients between the ages of 15-44 in 2009 and 44-64 and 64 and above in 2011 are out of control. This im...

Designs for nonlinear regression models depend on some prior information about the unknown parameters. There are three primary methods for accounting for this: The locally optimal designs, globally optimal Bayesian designs, and sequential procedures. If prior knowledge about the parameters is available from former experiments, Bayesian designs integrate this information most eeciently. If the e...

Regular vine copulas are a novel and very flexible class of dependence models. This paper presents a reversible jump MCMC strategy for Bayesian model selection and inference of regular vine copulas, which can select all levels of a regular vine copula simultaneously. This is a substantial improvement over existing frequentist and Bayesian strategies, which can only select in a sequential, level...

The sequential testing of more than two hypotheses has important applications in direct-sequence spread spectrum signal acquisition, multiple-resolution-element radar, and other areas. A useful sequential test which we term the MSPRT is studied in this paper. The test is shown to be a generalization of the Sequential Probability Ratio Test. Under Bayesian assumptions, it is argued that the MSPR...

[1] This paper uses sequential Bayesian techniques such as particle filters and smoothers to track in time both the non-volcanic tremor (NVT) source location on the plate interface and the angle of arrival via horizontal phase slowness. Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential tremor observations. These techniques provide a formulation whe...

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies the roles of learning and decision making during s...

Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predict...