Type Normal
Namespace tensorflow.distributions
Parent Distribution
Interfaces INormal
The Normal distribution with location `loc` and `scale` parameters. #### Mathematical details The probability density function (pdf) is, ```none
pdf(x; mu, sigma) = exp(-0.5 (x - mu)**2 / sigma**2) / Z
Z = (2 pi sigma**2)**0.5
``` where `loc = mu` is the mean, `scale = sigma` is the std. deviation, and, `Z`
is the normalization constant. The Normal distribution is a member of the [location-scale family](
https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
constructed as, ```none
X ~ Normal(loc=0, scale=1)
Y = loc + scale * X
``` #### Examples Examples of initialization of one or a batch of distributions.
Arguments are broadcast when possible.
Show Example
import tensorflow_probability as tfp
tfd = tfp.distributions # Define a single scalar Normal distribution.
dist = tfd.Normal(loc=0., scale=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 = tfd.Normal(loc=[1, 2.], scale=[11, 22.]) # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
# returning a length two tensor.
dist.prob([0, 1.5]) # Get 3 samples, returning a 3 x 2 tensor.
dist.sample([3])
Properties
Public properties
object allow_nan_stats get;
object allow_nan_stats_dyn get;
TensorShape batch_shape get;
object batch_shape_dyn get;
object dtype get;
object dtype_dyn get;
TensorShape event_shape get;
object event_shape_dyn get;
object loc get;
Distribution parameter for the mean.
object loc_dyn get;
Distribution parameter for the mean.
string name get;
object name_dyn get;
IDictionary<object, object> parameters get;
object parameters_dyn get;
object PythonObject get;
object reparameterization_type get;
object reparameterization_type_dyn get;
object scale get;
Distribution parameter for standard deviation.
object scale_dyn get;
Distribution parameter for standard deviation.