tf.contrib.distributions.Bernoulli.pmf()

tf.contrib.distributions.Bernoulli.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Bernoulli.pdf()

tf.contrib.distributions.Bernoulli.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Bernoulli.param_static_shapes()

tf.contrib.distributions.Bernoulli.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.Bernoulli.param_shapes()

tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Bernoulli.parameters

tf.contrib.distributions.Bernoulli.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Bernoulli.p

tf.contrib.distributions.Bernoulli.p

tf.contrib.distributions.Bernoulli.name

tf.contrib.distributions.Bernoulli.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.Bernoulli.mode()

tf.contrib.distributions.Bernoulli.mode(name='mode') Mode. Additional documentation from Bernoulli: Returns 1 if p > 1-p and 0 otherwise.

tf.contrib.distributions.Bernoulli.mean()

tf.contrib.distributions.Bernoulli.mean(name='mean') Mean.

tf.contrib.distributions.Bernoulli.log_survival_function()

tf.contrib.distributions.Bernoulli.log_survival_function(value, name='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: value: float or double T