Remove mentions of Wishart distribution in LKJ notebook.

Change capitalization

Change capitalization

Author: zaxtax

docs/source/notebooks/LKJ.ipynb

@@ -13,9 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Outside of the [beta](https://en.wikipedia.org/wiki/Beta_distribution)-[binomial](https://en.wikipedia.org/wiki/Binomial_distribution) model, the multivariate normal model is likely the most studied Bayesian model in history.  Unfortunately, as this [issue](https://github.com/pymc-devs/pymc3/issues/538) shows, `pymc3` cannot (yet) sample from the standard conjugate [normal-Wishart](https://en.wikipedia.org/wiki/Normal-Wishart_distribution) model.  Fortunately, `pymc3` *does* support sampling from the [LKJ distribution](http://www.sciencedirect.com/science/article/pii/S0047259X09000876).  This post will show how to fit a simple multivariate normal model using `pymc3` with an normal-LKJ prior.\n",
-    "\n",
-    "The normal-Wishart prior is conjugate for the multivariate normal model, so we can find the posterior distribution in closed form.  Even with this closed form solution, sampling from a multivariate normal model in `pymc3` is important as a building block for more complex models.\n",
+    "Outside of the [beta](https://en.wikipedia.org/wiki/Beta_distribution)-[binomial](https://en.wikipedia.org/wiki/Binomial_distribution) model, the multivariate normal model is likely the most studied Bayesian model in history. `PyMC3` supports sampling from the [LKJ distribution](http://www.sciencedirect.com/science/article/pii/S0047259X09000876). The LKJ distribution represents the distribution on correlation matrices and is conjugate to the multivariate normal distribution. This post will show how to fit a simple multivariate normal model using `pymc3` with a normal-LKJ prior.\n",
     "\n",
     "First, we generate some two-dimensional sample data."
    ]