scvi.core.distributions.NegativeBinomial

class scvi.core.distributions.NegativeBinomial(total_count=None, probs=None, logits=None, mu=None, theta=None, validate_args=True)[source]

Negative binomial distribution.

One of the following parameterizations must be provided:

  • (total_count, probs) where total_count is the number of failures until

the experiment is stopped and probs the success probability. - The (mu, theta) parameterization is the one used by scvi-tools. These parameters respectively control the mean and inverse dispersion of the distribution.

Parameters
total_count : Tensor, NoneOptional[Tensor] (default: None)

Number of failures until the experiment is stopped.

probs : Tensor, NoneOptional[Tensor] (default: None)

The success probability.

mu : Tensor, NoneOptional[Tensor] (default: None)

Mean of the distribution.

theta : Tensor, NoneOptional[Tensor] (default: None)

Inverse dispersion.

validate_args : boolbool (default: True)

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.

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)