tf.contrib.distributions.DirichletMultinomial.__init__()

tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=False, allow_nan_stats=True, name='DirichletMultinomial')

Initialize a batch of DirichletMultinomial distributions.

Args:

• n: Non-negative floating point tensor, whose dtype is the same as alpha. The shape is broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values.
• alpha: Positive floating point tensor, whose dtype is the same as n with shape broadcastable to [N1,..., Nm, k] m >= 0. Defines this as a batch of N1 x ... x Nm different k class Dirichlet multinomial distributions.
• validate_args: Boolean, default False. Whether to assert valid values for parameters alpha and n, and x in prob and log_prob. If False, correct behavior is not guaranteed.
• allow_nan_stats: 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.

• name: The name to prefix Ops created by this distribution class.

• Examples:

# Define 1-batch of 2-class Dirichlet multinomial distribution,
# also known as a beta-binomial.
dist = DirichletMultinomial(2.0, [1.1, 2.0])

# Define a 2-batch of 3-class distributions.
dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])