NDLI: An Informative Interpretation of Decision Theory: Scalar Performance Measures for Binary Decisions

Content Provider	IEEE Xplore Digital Library
Author	Polcari, J.
Copyright Year	2013
Abstract	A previous formulation for the application of information accounting to binary decision theory is extended to permit the quality of the decision to be quantitatively measured by evaluation of the underlying informational support. Both a single exemplar measure of information, separability, and its ensemble average equivalent, separation, are shown to measure the information support for decision quality (i.e., how well-informed is the decision), rather than the information support for decision adjudication (i.e., which hypothesis is the better choice) provided by predecision information measures. When compared to the traditional receiver operating characteristic, these measures present several functional advantages. They are scalar in nature, and may be directly optimized over secondary parameters, as well as being rigorously well posed and universally comparable. They incorporate the effects of all relevant decision components (prior information, observational information, and decision rule) in a unified manner while still being easily related to the predecision information measures of log likelihood ratio and generalized signal-to-noise ratio. They can be applied equally well to individual trials or composite averages, and evaluation does not require knowledge of the underlying truth. Compared to false alarm-oriented methods for assessing decision performance, their construction reduces sensitivity to tail effects in the underlying distributions.
Starting Page	1456
Ending Page	1480
Page Count	25
File Size	10026944
File Format	PDF
ISSN	21693536
Volume Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision theory Bayes methods Signal to noise ratio Statistical analysis Data models Statistical Analysis Data Compression Decision Theory statistical analysis Data compression decision theory detection algorithms information measures information theory Kullback-Leibler divergence log likelihood ratio performance evaluation performance measures self-scaling property signal processing algorithms signal to noise ratio
Content Type	Text
Resource Type	Article
Subject	Computer Science Engineering Materials Science

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