tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.clone(name=None, **dist_args)

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.dtype The DType of Tensors handled by this Distribution.

tensorflow::Thread::Thread()

tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_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.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_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.Poisson.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: batch_shape: TensorShape, possibly unknown.

class tf.contrib.distributions.MultivariateNormalCholesky The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and a Cholesky factor chol. Providing the Cholesky factor allows for O(k^2) pdf evaluation and sampling, and requires O(k^2) storage.

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False) Sample n times and reshape the outer 2 axes so rank does not change. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.distributions.NormalWithSoftplusSigma.is_reparameterized

class tf.contrib.distributions.LaplaceWithSoftplusScale Laplace with softplus applied to scale.