The scalar Normal distribution with mean and stddev parameters mu, sigma.

#### Mathematical details

The PDF of this distribution is:

```f(x) = sqrt(1/(2*pi*sigma^2)) exp(-(x-mu)^2/(2*sigma^2))```

#### Examples

Examples of initialization of one or a batch of distributions.

```python
# Define a single scalar Normal distribution.
dist = tf.contrib.distributions.Normal(mu=0., sigma=3.)

# Evaluate the cdf at 1, returning a scalar.
dist.cdf(1.)

# Define a batch of two scalar valued Normals.
# The first has mean 1 and standard deviation 11, the second 2 and 22.
dist = tf.contrib.distributions.Normal(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.
dist.pdf([0, 1.5])

# Get 3 samples, returning a 3 x 2 tensor.
dist.sample([3])
```

Arguments are broadcast when possible.

```python
# Define a batch of two scalar valued Normals.
# Both have mean 1, but different standard deviations.
dist = tf.contrib.distributions.Normal(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)
```
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#### `tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='Normal')` {#Normal.__init__}

Construct Normal distributions with mean and stddev `mu` and `sigma`.

The parameters `mu` and `sigma` must be shaped in a way that supports
broadcasting (e.g. `mu + sigma` is a valid operation).

##### Args:


*  <b>`mu`</b>: Floating point tensor, the means of the distribution(s).
*  <b>`sigma`</b>: Floating point tensor, the stddevs of the distribution(s).
    sigma must contain only positive values.
*  <b>`validate_args`</b>: `Boolean`, default `False`.  Whether to assert that
    `sigma > 0`. If `validate_args` is `False`, correct output is not
    guaranteed when input is invalid.
*  <b>`allow_nan_stats`</b>: `Boolean`, default `True`.  If `False`, raise an
    exception if a statistic (e.g. mean/mode/etc...) is undefined for any
    batch member.  If `True`, batch members with valid parameters leading to
    undefined statistics will return NaN for this statistic.
*  <b>`name`</b>: The name to give Ops created by the initializer.

##### Raises:


*  <b>`TypeError`</b>: if mu and sigma are different dtypes.


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#### `tf.contrib.distributions.Normal.allow_nan_stats` {#Normal.allow_nan_stats}

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense.  E.g., the variance
of a Cauchy distribution is infinity.  However, sometimes the
statistic is undefined, e.g., if a distribution's pdf does not achieve a
maximum within the support of the distribution, the mode is undefined.
If the mean is undefined, then by definition the variance is undefined.
E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
it is either + or - infinity), so the variance = E[(X - mean)^2] is also
undefined.

##### Returns:


*  <b>`allow_nan_stats`</b>: Python boolean.


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#### `tf.contrib.distributions.Normal.batch_shape(name='batch_shape')` {#Normal.batch_shape}

Shape of a single sample from a single event index as a 1-D `Tensor`.

The product of the dimensions of the `batch_shape` is the number of
independent distributions of this kind the instance represents.

##### Args:


*  <b>`name`</b>: name to give to the op

##### Returns:


*  <b>`batch_shape`</b>: `Tensor`.


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#### `tf.contrib.distributions.Normal.cdf(value, name='cdf', **condition_kwargs)` {#Normal.cdf}

Cumulative distribution function.

Given random variable `X`, the cumulative distribution function `cdf` is:

```
cdf(x) := P[X <= x]
```

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.


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#### `tf.contrib.distributions.Normal.copy(**override_parameters_kwargs)` {#Normal.copy}

Creates a deep copy of the distribution.

Note: the copy distribution may continue to depend on the original
intialization arguments.

##### Args:


*  <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization
    arguments to override with new values.

##### Returns:


*  <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union
    of self.parameters and override_parameters_kwargs, i.e.,
    `dict(self.parameters, **override_parameters_kwargs)`.


- - -

#### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}

The `DType` of `Tensor`s handled by this `Distribution`.


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#### `tf.contrib.distributions.Normal.entropy(name='entropy')` {#Normal.entropy}

Shannon entropy in nats.


- - -

#### `tf.contrib.distributions.Normal.event_shape(name='event_shape')` {#Normal.event_shape}

Shape of a single sample from a single batch as a 1-D int32 `Tensor`.

##### Args:


*  <b>`name`</b>: name to give to the op

##### Returns:


*  <b>`event_shape`</b>: `Tensor`.


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#### `tf.contrib.distributions.Normal.get_batch_shape()` {#Normal.get_batch_shape}

Shape of a single sample from a single event index as a `TensorShape`.

Same meaning as `batch_shape`. May be only partially defined.

##### Returns:


*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.


- - -

#### `tf.contrib.distributions.Normal.get_event_shape()` {#Normal.get_event_shape}

Shape of a single sample from a single batch as a `TensorShape`.

Same meaning as `event_shape`. May be only partially defined.

##### Returns:


*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.


- - -

#### `tf.contrib.distributions.Normal.is_continuous` {#Normal.is_continuous}




- - -

#### `tf.contrib.distributions.Normal.is_reparameterized` {#Normal.is_reparameterized}




- - -

#### `tf.contrib.distributions.Normal.is_scalar_batch(name='is_scalar_batch')` {#Normal.is_scalar_batch}

Indicates that `batch_shape == []`.

##### Args:


*  <b>`name`</b>: The name to give this op.

##### Returns:


*  <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`.


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#### `tf.contrib.distributions.Normal.is_scalar_event(name='is_scalar_event')` {#Normal.is_scalar_event}

Indicates that `event_shape == []`.

##### Args:


*  <b>`name`</b>: The name to give this op.

##### Returns:


*  <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`.


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#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Normal.log_cdf}

Log cumulative distribution function.

Given random variable `X`, the cumulative distribution function `cdf` is:

```
log_cdf(x) := Log[ P[X <= x] ]
```

Often, a numerical approximation can be used for `log_cdf(x)` that yields
a more accurate answer than simply taking the logarithm of the `cdf` when
`x << -1`.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.


- - -

#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Normal.log_pdf}

Log probability density function.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.

##### Raises:


*  <b>`TypeError`</b>: if not `is_continuous`.


- - -

#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Normal.log_pmf}

Log probability mass function.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.

##### Raises:


*  <b>`TypeError`</b>: if `is_continuous`.


- - -

#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob', **condition_kwargs)` {#Normal.log_prob}

Log probability density/mass function (depending on `is_continuous`).

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.


- - -

#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Normal.log_survival_function}

Log survival function.

Given random variable `X`, the survival function is defined:

```
log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]
```

Typically, different numerical approximations can be used for the log
survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type
    `self.dtype`.


- - -

#### `tf.contrib.distributions.Normal.mean(name='mean')` {#Normal.mean}

Mean.


- - -

#### `tf.contrib.distributions.Normal.mode(name='mode')` {#Normal.mode}

Mode.


- - -

#### `tf.contrib.distributions.Normal.mu` {#Normal.mu}

Distribution parameter for the mean.


- - -

#### `tf.contrib.distributions.Normal.name` {#Normal.name}

Name prepended to all ops created by this `Distribution`.


- - -

#### `tf.contrib.distributions.Normal.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Normal.param_shapes}

Shapes of parameters given the desired shape of a call to `sample()`.

Subclasses should override static method `_param_shapes`.

##### Args:


*  <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to
    `sample()`.
*  <b>`name`</b>: name to prepend ops with.

##### Returns:

  `dict` of parameter name to `Tensor` shapes.


- - -

#### `tf.contrib.distributions.Normal.param_static_shapes(cls, sample_shape)` {#Normal.param_static_shapes}

param_shapes with static (i.e. TensorShape) shapes.

##### Args:


*  <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call
    to `sample()`.

##### Returns:

  `dict` of parameter name to `TensorShape`.

##### Raises:


*  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.


- - -

#### `tf.contrib.distributions.Normal.parameters` {#Normal.parameters}

Dictionary of parameters used to instantiate this `Distribution`.


- - -

#### `tf.contrib.distributions.Normal.pdf(value, name='pdf', **condition_kwargs)` {#Normal.pdf}

Probability density function.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.

##### Raises:


*  <b>`TypeError`</b>: if not `is_continuous`.


- - -

#### `tf.contrib.distributions.Normal.pmf(value, name='pmf', **condition_kwargs)` {#Normal.pmf}

Probability mass function.

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.

##### Raises:


*  <b>`TypeError`</b>: if `is_continuous`.


- - -

#### `tf.contrib.distributions.Normal.prob(value, name='prob', **condition_kwargs)` {#Normal.prob}

Probability density/mass function (depending on `is_continuous`).

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
    values of type `self.dtype`.


- - -

#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Normal.sample}

Generate samples of the specified shape.

Note that a call to `sample()` without arguments will generate a single
sample.

##### Args:


*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
*  <b>`seed`</b>: Python integer seed for RNG
*  <b>`name`</b>: name to give to the op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:


*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.


- - -

#### `tf.contrib.distributions.Normal.sigma` {#Normal.sigma}

Distribution parameter for standard deviation.


- - -

#### `tf.contrib.distributions.Normal.std(name='std')` {#Normal.std}

Standard deviation.


- - -

#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function', **condition_kwargs)` {#Normal.survival_function}

Survival function.

Given random variable `X`, the survival function is defined:

```
survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
```

##### Args:


*  <b>`value`</b>: `float` or `double` `Tensor`.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  Tensor` of shape `sample_shape(x) + self.batch_shape` with values of type
    `self.dtype`.


- - -

#### `tf.contrib.distributions.Normal.validate_args` {#Normal.validate_args}

Python boolean indicated possibly expensive checks are enabled.


- - -

#### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance}

Variance.


