Weizmann Logo
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



TR14-021 | 18th February 2014 05:43

Testing probability distributions underlying aggregated data


Authors: Clement Canonne, Ronitt Rubinfeld
Publication: 18th February 2014 19:28
Downloads: 3361


In this paper, we analyze and study a hybrid model for testing and learning probability distributions. Here, in addition to samples, the testing algorithm is provided with one of two different types of oracles to the unknown distribution $D$ over $[n]$. More precisely, we define both the dual and extended dual access models, in which the algorithm $A$ can both sample from $D$ and respectively, for any $i\in[n]$,
- query the probability mass $D(i)$ (query access); or
- get the total mass of $\{1,\dots,i\}$, i.e. $\sum_{j=1}^i D(j)$ (cumulative access)
These two models, by generalizing the previously studied sampling and query oracle models, allow us to bypass the strong lower bounds established for a number of problems in these settings, while capturing several interesting aspects of these problems -- and providing new insight on the limitations of the models. Finally, we show that while the testing algorithms can be in most cases strictly more efficient, some tasks remain hard even with this additional power.

ISSN 1433-8092 | Imprint