Distribution property testing deals with what information can be deduced about an unknown distribution over {1,…,n}, where we are only allowed to obtain a relatively small number of independent samples from the distribution. In the basic model the algorithm may only base its decision on receiving a sequence of samples from the distribution, while in the conditional model the algorithm may also request samples out of the conditional distribution over subsets of {1,…,n}.

A test has to distinguish a distribution satisfying a given property from a distribution that is far in the variation distance from satisfying it. A range of properties such as monotonicity and log-concavity has been unified under the banner of L-decomposable properties. Here we improve upon the basic model test for all such properties, as well as provide a new test under the conditional model whose number of queries does not directly depend on n. We also provide a conditional model test for a wider range of properties, that in particular yields tolerant testing for all L-decomposable properties. For tolerant testing conditional samples are essential, as an efficient test in the basic model is known not to exist.

Our main mechanism is a way of efficiently producing a partition of {1,…,n} into intervals satisfying a small-weight requirement with respect to the unknown distribution. Also, we show that investigating just one such partition is sufficient for solving the testing question, as opposed to prior works where a search for the “correct” partition was performed.

Joint work with Oded Lachish and Yadu Vasudev.