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.

Detection Schemes Based on Local Optimality and Sequential Criterion: 1. Threshold Analysis

Abstract: In this paper, a sequential detection scheme is proposed as a combination of a novel weak-signal and a locally optimum(LO) detection schemes. In Part 1, we propose a novel sequential detection scheme for weak signals and show some interesting threshold properties and examples. In Part 2, the performance of the proposed sequential detection scheme is compared with that of the fixed sample size(FSS) test, sequential probability ratio test (SPRT), and truncated sequential probability ratio test(TSPRT).

Application of MSET and SPRT model for high pressure nitrogen system PHM

Abstract: To improve the reliability and availability, prognostics and health management technology was applied to the high pressure nitrogen system. This paper presents a multivariate state estimation techniques and sequential probability ratio test model to predict equipment health. In the approach, correlation model among monitoring parameters in normal work condition is constructed firstly. Then, according to the similarities between the current observed feature vector and each history feature vector contained in process memory matrix, estimation of the current feature vector is calculated by using MSET, and residual signal between the current feature vector and its estimation is obtained in turn. Finally, mean test and variance test for the residual signal is executed by using SPRT, and work condition of the system is pronounced. Use the real- time data to test the model, the result showed that using this method can obtain good effect.

Bayesian Statistical Model Checking for Multi-agent Systems using HyperPCTL*

Abstract: In this paper, we present a Bayesian method for statistical model checking (SMC) of probabilistic hyperproperties specified in the logic HyperPCTL* on discrete-time Markov chains (DTMCs). While SMC of HyperPCTL* using sequential probability ratio test (SPRT) has been explored before, we develop an alternative SMC algorithm based on Bayesian hypothesis testing. In comparison to PCTL*, verifying HyperPCTL* formulae is complex owing to their simultaneous interpretation on multiple paths of the DTMC. In addition, extending the bottom-up model-checking algorithm of the non-probabilistic setting is not straight forward due to the fact that SMC does not return exact answers to the satisfiability problems of subformulae, instead, it only returns correct answers with high-confidence. We propose a recursive algorithm for SMC of HyperPCTL* based on a modified Bayes' test that factors in the uncertainty in the recursive satisfiability results. We have implemented our algorithm in a Python toolbox, HyProVer, and compared our approach with the SPRT based SMC. Our experimental evaluation demonstrates that our Bayesian SMC algorithm performs better both in terms of the verification time and the number of samples required to deduce satisfiability of a given HyperPCTL* formula.

Relearning Process for SPRT in Structural Change Detection of Time-Series Data

Abstract: This study proposes a relearning process for a prediction model after detecting structural change points. There are three problems with the detection of structural change points in time-series data: (1) how to generate a prediction model, (2) how to detect a structural change point rapidly, and (3) how the prediction model should relearn after detection. This article targets the third problem and proposes five relearning methods and a process that embeds the relearning process in the sequential probability ratio test. Two experiments, one using 20 generated data sets and the other TOPIX, which consists of 1104 time-series data points between 1991 and 2012, show that using past and future data after detecting the structural change points is helpful.