ml#ContinuousBernoulli.sample)
¶

    

_property_ ` stddev ` ¶

    

` support ` _= Interval(lower_bound=0.0, upper_bound=1.0)_ ¶

    

_property_ ` variance ` ¶

    

##  Dirichlet  ¶

_class_ ` torch.distributions.dirichlet. ` ` Dirichlet ` (  _concentration_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/dirichlet.html#Dirichlet) ¶

    

Bases:  ` torch.distributions.exp_family.ExponentialFamily  `

Creates a Dirichlet distribution parameterized by concentration `
concentration  ` .

Example:

    
    
    >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
    >>> m.sample()  # Dirichlet distributed with concentrarion concentration
    tensor([ 0.1046,  0.8954])
    

Parameters

    

**concentration** ( [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) –
concentration parameter of the distribution (often referred to as alpha)

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

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/dirichlet.html#Dirichlet.entropy) ¶

    

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

    

` has_rsample ` _= True_ ¶

    

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

    

_property_ ` mean ` ¶

    

` rsample ` (  _sample_shape=()_ )  [ [source]
](_modules/torch/distributions/dirichlet.html#Dirichlet.rsample) ¶

    

` support ` _= Simplex()_ ¶

    

_property_ ` variance ` ¶

    

##  Exponential  ¶

_class_ ` torch.distributions.exponential. ` ` Exponential ` (  _rate_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/exponential.html#Exponential) ¶

    

Bases:  ` torch.distributions.exp_family.ExponentialFamily  `

Creates a Exponential distribution parameterized by ` rate  ` .

Example:

    
    
    >>> m = Exponential(torch.tensor([1.0]))
    >>> m.sample()  # Exponential distributed with rate=1
    tensor([ 0.1046])
    

Parameters

    

**rate** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float
"\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor
"torch.Tensor") ) – rate = 1 / scale of the distribution

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

    

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

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/exponential.html#Exponential.entropy) ¶

    

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

    

` has_rsample ` _= True_ ¶

    

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

    

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

    

_property_ ` mean ` ¶

    

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

    

_property_ ` stddev ` ¶

    

` support ` _= GreaterThan(lower_bound=0.0)_ ¶

    

_property_ ` variance ` ¶

    

##  FisherSnedecor  ¶

_class_ ` torch.distributions.fishersnedecor. ` ` FisherSnedecor ` (  _df1_ ,
_df2_ , _validate_args=None_ )  [ [source]
](_modules/torch/distributions/fishersnedecor.html#FisherSnedecor) ¶

    

Bases:  ` torch.distributions.distribution.Distribution  `

Creates a Fisher-Snedecor distribution parameterized by ` df1  ` and ` df2  `
.

Example:

    
    
    >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
    >>> m.sample()  # Fisher-Snedecor-distributed with df1=1 and df2=2
    tensor([ 0.2453])
    

Parameters

    

  * **df1** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – degrees of freedom parameter 1 

  * **df2** (