[ENH] histogram distribution

[fkiraly]

For distribution estimation, an important distribution type is the histogram distribution.

This will be parameterized by bins and bin densities.

[ShreeshaM07]

Just needed some clarification, the bins would be the upper bound for the interval it will be an array or if its a single value then it will be divided equally and the bin_density will have an array of same size with values for all the bins ranging from [0,1] interval as we want it to be a probability distribution. Or do we want to store the frequency in the bin_density and then divide by total frequency to bring it down to [0,1].

Do I understand the requirement correctly?

[fkiraly]

Do I understand the requirement correctly?

The exact specification has not been set out, so it would be great if you could make a few suggestions!

The suggestion on specifying by number of bins, or bin boundaries both make sense - in fact these are both so common that popular interfaces like pandas.cut allow for both, via a polymorphic interface: http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html

Concretely, it would be nice if you could spell out what the parameters would/should be.

Some complexity comes in through taking into account that we are looking at array distributions, so the bin boundaries might differ between marginal distributions.

[ShreeshaM07]

@fkiraly There were some merge conflicts in the init.py in distributions and distributions.rst that were troubling a little so I decided to make a new PR #382 instead, so there are no conflicts.

All the discussion regarding the Histogram distribution has been discussed in the PR #335.

[fkiraly]

Reopening to keep a reminder of "summarizing outcomes" (vectorization) from #382 in a new issue.

[ShreeshaM07]

Reopening to keep a reminder of "summarizing outcomes" (vectorization) from #382 in a new issue.

I have opened #405 summarizing the outcomes of vectorization.