g_prob ` (  _value_ )  [ [source]
](_modules/torch/distributions/cauchy.html#Cauchy.log_prob) ¶

    

_property_ ` mean ` ¶

    

` rsample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/cauchy.html#Cauchy.rsample) ¶

    

` support ` _= Real()_ ¶

    

_property_ ` variance ` ¶

    

##  Chi2  ¶

_class_ ` torch.distributions.chi2. ` ` Chi2 ` (  _df_ , _validate_args=None_
)  [ [source]  ](_modules/torch/distributions/chi2.html#Chi2) ¶

    

Bases:  ` torch.distributions.gamma.Gamma  `

Creates a Chi2 distribution parameterized by shape parameter  ` df  ` . This
is exactly equivalent to ` Gamma(alpha=0.5*df,  beta=0.5)  `

Example:

    
    
    >>> m = Chi2(torch.tensor([1.0]))
    >>> m.sample()  # Chi2 distributed with shape df=1
    tensor([ 0.1046])
    

Parameters

    

**df** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float
"\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor
"torch.Tensor") ) – shape parameter of the distribution

` arg_constraints ` _= {'df': GreaterThan(lower_bound=0.0)}_ ¶

    

_property_ ` df ` ¶

    

` expand ` (  _batch_shape_ , __instance=None_ )  [ [source]
](_modules/torch/distributions/chi2.html#Chi2.expand) ¶

    

##  ContinuousBernoulli  ¶

_class_ ` torch.distributions.continuous_bernoulli. ` ` ContinuousBernoulli `
(  _probs=None_ , _logits=None_ , _lims=(0.499_ , _0.501)_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli)
¶

    

Bases:  ` torch.distributions.exp_family.ExponentialFamily  `

Creates a continuous Bernoulli distribution parameterized by  ` probs  ` or  `
logits  ` (but not both).

The distribution is supported in [0, 1] and parameterized by ‘probs’ (in
(0,1)) or ‘logits’ (real-valued). Note that, unlike the Bernoulli, ‘probs’
does not correspond to a probability and ‘logits’ does not correspond to log-
odds, but the same names are used due to the similarity with the Bernoulli.
See [1] for more details.

Example:

    
    
    >>> m = ContinuousBernoulli(torch.tensor([0.3]))
    >>> m.sample()
    tensor([ 0.2538])
    

Parameters

    

  * **probs** ( _Number_ _,_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – (0,1) valued parameters 

  * **logits** ( _Number_ _,_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – real valued parameters whose sigmoid matches ‘probs’ 

[1] The continuous Bernoulli: fixing a pervasive error in variational
autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019. [
https://arxiv.org/abs/1907.06845 ](https://arxiv.org/abs/1907.06845)

` arg_constraints ` _= {'logits': Real(), 'probs': Interval(lower_bound=0.0,
upper_bound=1.0)}_ ¶

    

` cdf ` (  _value_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.cdf)
¶

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.entropy)
¶

    

` expand ` (  _batch_shape_ , __instance=None_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.expand)
¶

    

` has_rsample ` _= True_ ¶

    

` icdf ` (  _value_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.icdf)
¶

    

` log_prob ` (  _value_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.log_prob)
¶

    

` logits ` [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.logits)
¶

    

_property_ ` mean ` ¶

    

_property_ ` param_shape ` ¶

    

` probs ` [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.probs)
¶

    

` rsample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.rsample)
¶

    

` sample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/continuous_bernoulli.ht