DeepAI AI Chat
Log In Sign Up

Minimum Probability of Error of List M-ary Hypothesis Testing

by   Ehsan Asadi Kangarshahi, et al.

We study a variation of Bayesian M-ary hypothesis testing in which the test outputs a list of L candidates out of the M possible upon processing the observation. We study the minimum error probability of list hypothesis testing, where an error is defined as the event where the true hypothesis is not in the list output by the test. We derive two exact expressions of the minimum probability or error. The first is expressed as the error probability of a certain non-Bayesian binary hypothesis test, and is reminiscent of the meta-converse bound. The second, is expressed as the tail probability of the likelihood ratio between the two distributions involved in the aforementioned non-Bayesian binary hypothesis test.


page 1

page 2

page 3

page 4


Mismatched Binary Hypothesis Testing: Error Exponent Sensitivity

We study the problem of mismatched binary hypothesis testing between i.i...

Order Effects of Measurements in Multi-Agent Hypothesis Testing

All propositions from the set of events for an agent in a multi-agent sy...

Limits of Deepfake Detection: A Robust Estimation Viewpoint

Deepfake detection is formulated as a hypothesis testing problem to clas...

On the optimality of likelihood ratio test for prospect theory based binary hypothesis testing

In this letter, the optimality of the likelihood ratio test (LRT) is inv...

Sub-Gaussian Error Bounds for Hypothesis Testing

We interpret likelihood-based test functions from a geometric perspectiv...

Sequential Controlled Sensing for Composite Multihypothesis Testing

The problem of multi-hypothesis testing with controlled sensing of obser...

A hypothesis-testing perspective on the G-normal distribution theory

The G-normal distribution was introduced by Peng [2007] as the limiting ...