Based on isolated saliency or causal integration? toward a better understanding of human annotation process using multiple instance learning and sequential probability ratio test

2012 ◽  
Author(s):  
Chi-Chun Lee ◽  
Athanasios Katsamanis ◽  
Panayiotis G. Georgiou ◽  
Shrikanth S. Narayanan
Keyword(s):  
Multiple Instance Learning ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Probability Ratio ◽  
Annotation Process ◽  
Sequential Probability

scholarly journals The VPRT: A Sequential Testing Procedure Dominating the SPRT

1993 ◽  
Vol 9 (3) ◽  
pp. 431-450 ◽  
Author(s):  
Noel Cressie ◽  
Peter B. Morgan
Keyword(s):  
Sample Size ◽  
Decision Problem ◽  
Sequential Analysis ◽  
Testing Procedure ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Sequential Decision ◽  
Probability Ratio ◽  
Sampling Cost ◽  
Sequential Probability

Under more general assumptions than those usually made in the sequential analysis literature, a variable-sample-size-sequential probability ratio test (VPRT) of two simple hypotheses is found that maximizes the expected net gain over all sequential decision procedures. In contrast, Wald and Wolfowitz [25] developed the sequential probability ratio test (SPRT) to minimize expected sample size, but their assumptions on the parameters of the decision problem were restrictive. In this article we show that the expected net-gain-maximizing VPRT also minimizes the expected (with respect to both data and prior) total sampling cost and that, under slightly more general conditions than those imposed by Wald and Wolfowitz, it reduces to the one-observation-at-a-time sequential probability ratio test (SPRT). The ways in which the size and power of the VPRT depend upon the parameters of the decision problem are also examined.

Sign in / Sign up

Export Citation Format

Share Document