Abstract:
We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of ...Show MoreMetadata
Abstract:
We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of a hierarchy of biased random walks guided by a local sequential test which together lend adaptivity and agnosticism to the unknown noise model. We show that the proposed test plan is order optimal in both the population size and the error rate.
Published in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 06-11 June 2021
Date Added to IEEE Xplore: 13 May 2021
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Version Of Test ,
- Noise Model ,
- Adaptation Planning ,
- Unknown Model ,
- Test Plan ,
- Error Rate ,
- Random Walk ,
- Test Sequences ,
- Test Problems ,
- Unknown Distribution ,
- Biased Random Walk ,
- Cardinality ,
- Number Of Tests ,
- Time-based ,
- Error Probability ,
- Root Node ,
- Universal Constant ,
- Leaf Node ,
- Target Node ,
- Current Node ,
- Binary Tree ,
- Confidence Estimation
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Version Of Test ,
- Noise Model ,
- Adaptation Planning ,
- Unknown Model ,
- Test Plan ,
- Error Rate ,
- Random Walk ,
- Test Sequences ,
- Test Problems ,
- Unknown Distribution ,
- Biased Random Walk ,
- Cardinality ,
- Number Of Tests ,
- Time-based ,
- Error Probability ,
- Root Node ,
- Universal Constant ,
- Leaf Node ,
- Target Node ,
- Current Node ,
- Binary Tree ,
- Confidence Estimation
- Author Keywords
