Minimal-latency human action recognition using reliable-inference
Image and Vision Computing
Initiation and termination of integration in a decision process
Neural Networks
Optimal decision making on the basis of evidence represented in spike trains
Neural Computation
Generalization of sequential Wald's test for more than two hypotheses
MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
Distributed sequential algorithms for regional source localization
Automatica (Journal of IFAC)
Hi-index | 754.84 |
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 MSPRT approximates the much more complicated optimal test when error probabilities are small and expected stopping times are large. Bounds on error probabilities are derived, and asymptotic expressions for the stopping time and error probabilities are given. A design procedure is presented for determining the parameters of the MSPRT. Two examples involving Gaussian densities are included, and comparisons are made between simulation results and asymptotic expressions. Comparisons with Bayesian fixed sample size tests are also made, and it is found that the MSPRT requires two to three times fewer samples on average