Distribution Funsors

This interface provides a number of standard normalized probability distributions implemented as funsors.

class Distribution(*args)

Bases: funsor.terms.Funsor

Funsor backed by a PyTorch distribution object.

Parameters:*args – Distribution-dependent parameters. These can be either funsors or objects that can be coerced to funsors via to_funsor() . See derived classes for details.

dist_class = 'defined by derived classes'

eager_reduce(op, reduced_vars)

classmethod eager_log_prob(**params)

class BernoulliLogits(logits, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Bernoulli .

Parameters:

• logits (Funsor) – Log likelihood ratio of 1. This should equal log(p1 / p0).
• value (Funsor) – Optional observation in {0,1}.

dist_class

alias of pyro.distributions.torch.Bernoulli

Bernoulli(probs=None, logits=None, value='value')

Wraps pyro.distributions.Bernoulli .

This dispatches to either BernoulliProbs or BernoulliLogits to accept either probs or logits args.

Parameters:

• probs (Funsor) – Probability of 1.
• value (Funsor) – Optional observation in {0,1}.

class Beta(concentration1, concentration0, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Beta .

Parameters:

• concentration1 (Funsor) – Positive concentration parameter.
• concentration0 (Funsor) – Positive concentration parameter.
• value (Funsor) – Optional observation in (0,1).

dist_class

alias of pyro.distributions.torch.Beta

class Binomial(total_count, probs, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Binomial .

Parameters:

• total_count (Funsor) – Total number of trials.
• probs (Funsor) – Probability of each positive trial.
• value (Funsor) – Optional integer observation (encoded as “real”).

dist_class

alias of pyro.distributions.torch.Binomial

class Categorical(probs, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Categorical .

Parameters:

• probs (Funsor) – Probability vector over outcomes.
• value (Funsor) – Optional bouded integer observation.

dist_class

alias of pyro.distributions.torch.Categorical

class Delta(v, log_density=0, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Delta .

Parameters:

• v (Funsor) – The unique point of concentration.
• log_density (Funsor) – Optional density (used by transformed distributions).
• value (Funsor) – Optional observation of similar domain as v.

dist_class

alias of pyro.distributions.delta.Delta

class Dirichlet(concentration, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Dirichlet .

Parameters:

• concentration (Funsor) – Positive concentration vector.
• value (Funsor) – Optional observation in the unit simplex.

dist_class

alias of pyro.distributions.torch.Dirichlet

class DirichletMultinomial(concentration, total_count, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.DirichletMultinomial .

Parameters:

• concentration (Funsor) – Positive concentration vector.
• total_count (Funsor) – Total number of trials.
• value (Funsor) – Optional observation in the unit simplex.

dist_class

alias of pyro.distributions.conjugate.DirichletMultinomial

LogNormal(loc, scale, value='value')

Wraps pyro.distributions.LogNormal .

Parameters:

• loc (Funsor) – Mean of the untransformed Normal distribution.
• scale (Funsor) – Standard deviation of the untransformed Normal distribution.
• value (Funsor) – Optional real observation.

class Multinomial(total_count, probs, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Multinomial .

Parameters:

• probs (Funsor) – Probability vector over outcomes.
• total_count (Funsor) – Total number of trials.
• value (Funsor) – Optional value in the unit simplex.

dist_class

alias of pyro.distributions.torch.Multinomial

class Normal(loc, scale, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Normal .

Parameters:

• loc (Funsor) – Mean.
• scale (Funsor) – Standard deviation.
• value (Funsor) – Optional real observation.

dist_class

alias of pyro.distributions.torch.Normal

class MultivariateNormal(loc, scale_tril, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.MultivariateNormal .

Parameters:

• loc (Funsor) – Mean vector.
• scale_tril (Funsor) – Lower Cholesky factor of the covariance matrix.
• value (Funsor) – Optional real vector observation.

dist_class

alias of pyro.distributions.torch.MultivariateNormal

class Poisson(rate, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Poisson .

Parameters:

• rate (Funsor) – Mean parameter.
• value (Funsor) – Optional integer observation (coded as “real”).

dist_class

alias of pyro.distributions.torch.Poisson

class Gamma(concentration, rate, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Gamma .

Parameters:

• concentration (Funsor) – Positive concentration parameter.
• rate (Funsor) – Positive rate parameter.
• value (Funsor) – Optional positive observation.

dist_class

alias of pyro.distributions.torch.Gamma

class VonMises(loc, concentration, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.VonMises .

Parameters:

• loc (Funsor) – A location angle.
• concentration (Funsor) – Positive concentration parameter.
• value (Funsor) – Optional angular observation.

dist_class

alias of pyro.distributions.torch.VonMises