scvi.distributions.NegativeBinomialMixture

class scvi.distributions.NegativeBinomialMixture(mu1, mu2, theta1, mixture_logits, theta2=None, validate_args=False)[source]

Negative binomial mixture distribution.

See NegativeBinomial for further description of parameters.

Parameters
mu1 : TensorTensor

Mean of the component 1 distribution.

mu2 : TensorTensor

Mean of the component 2 distribution.

theta1 : TensorTensor

Inverse dispersion for component 1.

mixture_logits : TensorTensor

Logits scale probability of belonging to component 1.

theta2 : Tensor | NoneOptional[Tensor] (default: None)

Inverse dispersion for component 1. If None, assumed to be equal to theta1.

validate_args : boolbool (default: False)

Raise ValueError if arguments do not match constraints

Attributes

arg_constraints

batch_shape

Returns the shape over which parameters are batched.

event_shape

Returns the shape of a single sample (without batching).

has_enumerate_support

has_rsample

mean

Returns the mean of the distribution.

stddev

Returns the standard deviation of the distribution.

support

variance

Returns the variance of the distribution.

Methods

cdf(value)

Returns the cumulative density/mass function evaluated at value.

entropy()

Returns entropy of distribution, batched over batch_shape.

enumerate_support([expand])

Returns tensor containing all values supported by a discrete distribution.

expand(batch_shape[, _instance])

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape.

icdf(value)

Returns the inverse cumulative density/mass function evaluated at value.

log_prob(value)

Returns the log of the probability density/mass function evaluated at value.

mixture_probs()

rtype

TensorTensor

perplexity()

Returns perplexity of distribution, batched over batch_shape.

rsample([sample_shape])

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample([sample_shape])

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

sample_n(n)

Generates n samples or n batches of samples if the distribution parameters are batched.

set_default_validate_args(value)

Sets whether validation is enabled or disabled.