WIP - Rewrite rule simplification for sympy.stats

In [this blog post](http://matthewrocklin.com/blog/work/2012/12/11/Statistical-Simplification/) I motivate the use of rewrite rules for simplifying statistical distributions.  Statisticians know relations like if X and Y are standard normal random variables then X**2 has a ChiSquare distribution and X**2 + Y**2 has a ChiSquare distribution with two degrees of freedom.

Rewrite rules allow us to express and use this knowledge easily. 

This pull request constructs a `statsimp` function for the simplification of density calculations.  See `sympy/stats/simplify.py`.  The information for statistical transformations is separated from the algorithmic core.  Ideally someone should be able to look at `_rv_eqs` in `sympy/stats/simplify.py`, understand what is going on, and contribute more relations.  I encourage you to try this out.  Some distributions are evident here
http://www.math.wm.edu/~leemis/chart/UDR/UDR.html

This pull request builds on https://github.com/sympy/sympy/pull/1718 and https://github.com/sympy/sympy/pull/1720

This pull request is a work in progress.

In particular the information in `_rv_eqs` in https://github.com/mrocklin/sympy/blob/stats-rr/sympy/stats/simplify.py

```python
_rv_eqs = (
    (Normal(x, 0, 1)**2, ChiSquared(x, 1), [x]),
    (ChiSquared(x, m) + ChiSquared(y, n), ChiSquared(x, n+m), [x, y, n, m]),
)
```

Makes it so that the tests in https://github.com/mrocklin/sympy/blob/stats-rr/sympy/stats/tests/test_simplify.py pass

```python
def test_chisquared_three_degrees():
    X = Normal('X', 0, 1)
    Y = Normal('Y', 0, 1)
    Z = Normal('Z', 0, 1)
    assert next(statsimp(Density(X**2 + Y**2 + Z**2))) == \
            ChiSquaredDistribution(3)
```