clarify some language in the tensor shapes tutorial
"x = d.sample()\n",
"assert x.shape == d.batch_shape + d.event_shape\n",
"```\n",
- "Indices over `.batch_shape` denote independent random variables, whereas indices over `.event_shape` denote dependent random variables. Because the dependent random variables define probability together, the `.log_prob()` method only produces a single number for each event of shape `.event_shape`. Thus the total shape of `.log_prob()` is `.batch_shape`:\n",
+ "Indices over `.batch_shape` denote independent random variables, whereas indices over `.event_shape` denote dependent random variables (ie one draw from a distribution). Because the dependent random variables define probability together, the `.log_prob()` method only produces a single number for each event of shape `.event_shape`. Thus the total shape of `.log_prob()` is `.batch_shape`:\n",
"```py\n",
"assert d.log_prob(x).shape == d.batch_shape\n",
"```\n",
"source": [
"### Reshaping distributions <a class=\"anchor\" id=\"Reshaping-distributions\"></a>\n",
"\n",
- "In Pyro you can treat a univariate distribution as multivariate by calling the `.independent(_)` property."
+ "In Pyro you can treat a univariate distribution as multivariate by calling the [.independent(n)](http://docs.pyro.ai/en/0.2.1-release/distributions.html#pyro.distributions.torch_distribution.TorchDistributionMixin.independent) property where `n` is the number of batch dimensions (from the right) to declare as *dependent*."
]
},
{
