Multivariate normal with discrete approximation

[Mv77]

#120 Requests methods to deal with correlated discretized RVs.

This PR adds a multivariate normal distribution class, with a discretization method.

The idea for approximating an N-variate normal is:

• Construct a discretization for N independent standard normals.
• Join them as in CombineIndepDstns, taking the product of their grids (so you get an N-dimensional grid) and multiplying their pmfs.
• Rotate the grid using the eigendecomposition so that the resulting grid has the VCOV matrix that you want.

I have not found a citation for this, and I'm not familiar with the literature, so I'm not sure if this is inaccurate or inefficient. It seems to work well in my tests.

A multivariate log-normal like the one requested in #120 should be easily derivable from this.

@llorracc @sbenthall what do you think?

• Tests for new functionality/models or Tests to reproduce the bug-fix in code.
• Updated documentation of features that add new functionality.
• Update CHANGELOG.md with major/minor changes.

[Mv77]

Here's an example of how it looks:

The black dots are draws and the red are the discretized X's. Both from a negatively correlated multivariate normal.

[llorracc]

@Mv77 this looks great! I'd propose that a good test of it would be to implement the multivariate lognormal you mention as an easy application, because it should be easy to compare that to something that surely must exist which is some kind of tool for calculating the CDFs of continuous multivariate lognormals.

[llorracc]

@Mv77, I'd like to merge this in if you will resolve the conflicts.

[Mv77]

@Mv77, I'd like to merge this in if you will resolve the conflicts.

Great, I've just resolved them. This should be ready to merge after tests pass.

[codecov-commenter]

Codecov Report

Merging #948 (54accc5) into master (6d157ea) will increase coverage by 0.09%.
The diff coverage is 87.87%.

@@            Coverage Diff             @@
##           master     #948      +/-   ##
==========================================
+ Coverage   71.67%   71.76%   +0.09%     
==========================================
  Files          63       63              
  Lines        9197     9256      +59     
==========================================
+ Hits         6592     6643      +51     
- Misses       2605     2613       +8     

Impacted Files	Coverage Δ
HARK/distribution.py	79.34% <74.19%> (-0.53%)	⬇️
HARK/tests/test_approxDstns.py	100.00% <100.00%> (ø)
HARK/tests/test_distribution.py	100.00% <100.00%> (ø)
HARK/ConsumptionSaving/ConsIndShockModelFast.py	51.05% <0.00%> (ø)

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