Random Variables

The normal PyMC3 programmer will typically not need to interact with these classes, except possibly when debugging. Otherwise they are primarily of interest to developers.

class pymc3.model.PyMC3Variable(type, owner=None, index=None, name=None)

Class to wrap Theano TensorVariable for custom behavior.

class pymc3.model.ValueGradFunction(costs, grad_vars, extra_vars=None, *, dtype=None, casting='no', compute_grads=True, **kwargs)

Create a theano function that computes a value and its gradient.

Parameters

costs: list of theano variables

We compute the weighted sum of the specified theano values, and the gradient of that sum. The weights can be specified with ValueGradFunction.set_weights.

grad_vars: list of named theano variables or None

The arguments with respect to which the gradient is computed.

extra_vars: list of named theano variables or None

Other arguments of the function that are assumed constant. They are stored in shared variables and can be set using set_extra_values.

dtype: str, default=theano.config.floatX

The dtype of the arrays.

casting: {‘no’, ‘equiv’, ‘save’, ‘same_kind’, ‘unsafe’}, default=’no’

Casting rule for casting grad_args to the array dtype. See numpy.can_cast for a description of the options. Keep in mind that we cast the variables to the array and back from the array dtype to the variable dtype.

compute_grads: bool, default=True

If False, return only the logp, not the gradient.

kwargs

Extra arguments are passed on to theano.function.

Attributes

size: int

The number of elements in the parameter array.

profile: theano profiling object or None

The profiling object of the theano function that computes value and gradient. This is None unless profile=True was set in the kwargs.

array_to_dict(array)

Convert an array to a dictionary containing the grad_vars.

array_to_full_dict(array)

Convert an array to a dictionary with grad_vars and extra_vars.

dict_to_array(point)

Convert a dictionary with values for grad_vars to an array.

property profile

Profiling information of the underlying theano function.

class pymc3.model.FreeRV(type=None, owner=None, index=None, name=None, distribution=None, total_size=None, model=None)

Unobserved random variable that a model is specified in terms of.

Parameters

type: theano type (optional)

owner: theano owner (optional)

name: str

distribution: Distribution

model: Model

total_size: scalar Tensor (optional)

needed for upscaling logp

property init_value

Convenience attribute to return tag.test_value

class pymc3.model.ObservedRV(type=None, owner=None, index=None, name=None, data=None, distribution=None, total_size=None, model=None)

Observed random variable that a model is specified in terms of. Potentially partially observed.

Parameters

type: theano type (optional)

owner: theano owner (optional)

name: str

distribution: Distribution

model: Model

total_size: scalar Tensor (optional)

needed for upscaling logp

property init_value

Convenience attribute to return tag.test_value

class pymc3.model.MultiObservedRV(name, data, distribution, total_size=None, model=None)

Observed random variable that a model is specified in terms of. Potentially partially observed.

Parameters

type: theano type (optional)

owner: theano owner (optional)

name: str

distribution: Distribution

model: Model

total_size: scalar Tensor (optional)

needed for upscaling logp

class pymc3.model.TransformedRV(type=None, owner=None, index=None, name=None, distribution=None, model=None, transform=None, total_size=None)

Parameters

type: theano type (optional)

owner: theano owner (optional)

name: str

distribution: Distribution

model: Model

total_size: scalar Tensor (optional)

needed for upscaling logp

property init_value

Convenience attribute to return tag.test_value