Add function for calculating quantiles of weighed samples.

[BenZickel]

The Problem

When calculating distribution quantiles based on samples sampled from a guide it is often desirable to correct for the non-uniform gap between the model log-probability and the guide log-probability (this gap is already computed by the pyro.infer.importance.Importance module).

Currently there is only support for the below:

• Calculating quantiles from equally weighed samples by using pyro.ops.stats.quantile which interpolates quantiles over the sorted samples.
• Sampling with non-uniform weights using pyro.distributions.empirical.Empirical which does not support interpolation.

The Proposed Solution

Add a function pyro.ops.stats.weighed_quantile which supports calculating interpolated quantiles with non-uniform weights. The implementation is similar to pyro.ops.stats.quantile but with different weights generated for each index of the input, according to the indices that sort that specific index.

[BenZickel]

@fehiepsi, I've fixed the testing errors and now make format and make test pass successfully.
Sorry for not getting it right earlier.

[fritzo]

Hi @BenZickel looks great! I wish we had designed Pyro for more native support of weighted sets of samples, but this is a step in the right direction.

Could you add a simple unit test of this function, similar to test_quantiles() in tests/ops/test_stats.py?

[fritzo]

Looks great, just one little comment on Tuple[float] vs Tuple[float, ...]