Computer Science – Information Theory
Scientific paper
2011-01-31
Computer Science
Information Theory
38 pages, 8 figures, submitted to IEEE Transactions on Information Theory, and part of the paper appeared in the proceedings o
Scientific paper
We study a hypothesis testing problem in which data is compressed distributively and sent to a detector that seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning perform well and often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to "test against conditional independence." We then use this optimality result to give an outer bound for a more general class of instances of the problem.
Rahman Md Saifur
Wagner Aaron B.
No associations
LandOfFree
Optimality of Binning for Distributed Hypothesis Testing 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 Optimality of Binning for Distributed Hypothesis Testing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Optimality of Binning for Distributed Hypothesis Testing will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-648233