Sequential probability ratio test

About: Sequential probability ratio test is a research topic. Over the lifetime, 1248 publications have been published within this topic receiving 22355 citations.

On-line quality monitoring in short-circuit gas metal arc welding

Abstract: This paper addresses the problems involved in the automatic monitoring of the weld quality produced by robotized short-arc welding. A simple statistical change detection algorithm for the weld quality, the repeated Sequential Probability Ratio Test (SPRT), was used. The algorithm may similarly be viewed as a cumulative sum (CUSUM) type test, and is well-suited to detecting sudden minor changes in the monitored test statistic. The test statistic is based on the variance of the weld voltage, wherein it will be shown that the variance decreases when the welding process is not operating under optimal conditions. The performance of the algorithm is assessed through the use of experimental data. The results obtained from the algorithm show that it is possible to detect changes in weld quality automatically and on-line.

Sequential Analysis, Direct Method

Abstract: This paper describes from first principles the direct calculation of the operating characteristic function, O.C., the probability of accepting the hypothesis θ = θ0, and the average sample size, A.S.N., required to terminate the test, for any truncated sequential test once the acceptance, rejection, and the continuation regions are specified at each stage. What is needed is to regard a sequential test as a step by step random walk, which is a Markov chain. The method is contrasted with Wald's and two examples are included.

Bearing-only SLAM Using a SPRT Based Gaussian Sum Filter

Abstract: Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.

Nearly-optimal sequential tests for finitely many parameter values

Abstract: Combinations of one-sided sequential probability ratio tests (SPRT's) are shown to be "nearly optimal" for problems involving a finite number of possible underlying distributions. Subject to error probability constraints, expected sample sizes (or weighted averages of them) are minimized to within o(1) asymptotically. For sequential decision problems, simple explicit procedures are proposed which "do exactly what a Bayes solution would do" with probability approaching one as the cost per observation, c, goes to zero. Exact computations for a binomial testing problem show that efficiencies of about 97% are obtained in some "small-sample" cases.