Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms

Details Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms

2011-07-22

arxiv.org/abs/1107.4540v1

Computer Science

Information Theory

Scientific paper

We consider the problem of detecting a small subset of defective items from a large set via non-adaptive "random pooling" group tests. We consider both the case when the measurements are noiseless, and the case when the measurements are noisy (the outcome of each group test may be independently faulty with probability q). Order-optimal results for these scenarios are known in the literature. We give information-theoretic lower bounds on the query complexity of these problems, and provide corresponding computationally efficient algorithms that match the lower bounds up to a constant factor. To the best of our knowledge this work is the first to explicitly estimate such a constant that characterizes the gap between the upper and lower bounds for these problems.

Affiliated with

Also associated with

No associations

Rating

Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-676901