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+Student's t distribution with degree-of-freedom parameter df.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+`f(t) = gamma((df+1)/2)/sqrt(df*pi)/gamma(df/2)*(1+t^2/df)^(-(df+1)/2)`
+
+#### Examples
+
+Examples of initialization of one or a batch of distributions.
+
+```python
+# Define a single scalar Student t distribution.
+single_dist = tf.contrib.distributions.StudentT(df=3)
+
+# Evaluate the pdf at 1, returning a scalar Tensor.
+single_dist.pdf(1.)
+
+# Define a batch of two scalar valued Student t's.
+# The first has degrees of freedom 2, mean 1, and scale 11.
+# The second 3, 2 and 22.
+multi_dist = tf.contrib.distributions.StudentT(df=[2, 3],
+                                               mu=[1, 2.],
+                                               sigma=[11, 22.])
+
+# Evaluate the pdf of the first distribution on 0, and the second on 1.5,
+# returning a length two tensor.
+multi_dist.pdf([0, 1.5])
+
+# Get 3 samples, returning a 3 x 2 tensor.
+multi_dist.sample(3)
+```
+
+Arguments are broadcast when possible.
+
+```python
+# Define a batch of two Student's t distributions.
+# Both have df 2 and mean 1, but different scales.
+dist = tf.contrib.distributions.StudentT(df=2, mu=1, sigma=[11, 22.])
+
+# Evaluate the pdf of both distributions on the same point, 3.0,
+# returning a length 2 tensor.
+dist.pdf(3.0)
+```
+- - -
+
+#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, name='StudentT')` {#StudentT.__init__}
+
+Construct Student's t distributions.
+
+The distributions have degree of freedom `df`, mean `mu`, and scale `sigma`.
+
+The parameters `df`, `mu`, and `sigma` must be shaped in a way that supports
+broadcasting (e.g. `df + mu + sigma` is a valid operation).
+
+##### Args:
+
+
+*  <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the
+    distribution(s). `df` must contain only positive values.
+*  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
+*  <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the
+    distribution(s). `sigma` must contain only positive values.
+    Note that `sigma` is not the standard deviation of this distribution.
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if mu and sigma are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.df` {#StudentT.df}
+
+Degrees of freedom in these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.dtype` {#StudentT.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy}
+
+The entropy of Student t distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.get_batch_shape()` {#StudentT.get_batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.get_event_shape()` {#StudentT.get_event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.is_reparameterized` {#StudentT.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.log_pdf(x, name='log_pdf')` {#StudentT.log_pdf}
+
+Log pdf of observations in `x` under these Student's t-distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.mean` {#StudentT.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.mu` {#StudentT.mu}
+
+Locations of these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.name` {#StudentT.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.pdf(x, name='pdf')` {#StudentT.pdf}
+
+The PDF of observations in `x` under these Student's t distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
+    `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sample(n, seed=None, name='sample')` {#StudentT.sample}
+
+Sample `n` observations from the Student t Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sigma` {#StudentT.sigma}
+
+Scaling factors of these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.variance` {#StudentT.variance}
+
+
+
+