We propose a Bayesian decision-theoretic model of a fully sequential experiment in which the real-valued primary end point is observed with delay. The goal is to identify the sequential experiment which maximises the expected benefits of technology adoption decisions, minus sampling costs. The solution yields a unified policy defining the optimal ‘do not experiment’/‘fixed sample size experimen...

We give two algorithms to randomly permute a linked list of length n in place using O(n logn) time and O(logn) stack space in both the expected case and the worst case. The first algorithm uses well-known sequential random sampling, and the second uses inverted sequential random sampling.  1999 Published by Elsevier Science B.V. All rights reserved.

We propose a sequential sampling policy for noisy discrete global optimization and ranking and selection, in which we aim to efficiently explore a finite set of alternatives before selecting an alternative as best when exploration stops. Each alternative may be characterized by a multi-dimensional vector of categorical and numerical attributes and has independent normal rewards. We use a Bayesi...

We generalize the rationalize inattention framework proposed by Sims (2010) to allow for cost functions other than Shannon’s mutual information. Unlike other general treatments of the problem, our particular concern is with the additional structure that results when a large number of successive samples of information about the decision situation (each only minimally informative by itself) can b...

A central problem in information retrieval is the automated classification of text documents. While many existing methods achieve good levels of performance, they generally require levels of computation that prevent them from making sufficiently fast decisions in some applied setting. Using insights gained from examining the way humans make fast decisions when classifying text documents, two ne...

In this paper, we present a new technique for detecting moving targets from image sequences captured by moving sensors. Feature points are detected and tracked through the image sequences. A validity vector is used to describe the consistency of feature trajectories with sensor motion. By using the sequential importance sampling method, an approximation to the posterior distribution of the sens...

We consider the task of filtering dynamical systems observed in noise by means of sequential importance sampling when the proposal is restricted to the innovation components of the state. It is argued that the unmodified sequential importance sampling/resampling (SIR) algorithm may yield high variance estimates of the posterior in this case, resulting in poor performance when e.g. in visual tra...

S sampling problems arise in stochastic simulation and many other applications. Sampling is used to infer the unknown performance of several alternatives before one alternative is selected as best. This paper presents new economically motivated fully sequential sampling procedures to solve such problems, called economics of selection procedures. The optimal procedure is derived for comparing a ...

We consider Bayesian information collection, in which a measurement policy collects information to support a future decision. This framework includes ranking and selection, continuous global optimization, and many other problems in sequential experimental design. We give a sufficient condition under which measurement policies sample each measurement type infinitely often, ensuring consistency, ...

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper devel...