ParetoNBDRV

class pymc_marketing.clv.distributions.ParetoNBDRV(name=None, ndim_supp=None, ndims_params=None, dtype=None, inplace=None, signature=None)

Methods

ParetoNBDRV.L_op(inputs, outputs, output_grads)	Construct a graph for the L-operator.
ParetoNBDRV.R_op(inputs, eval_points)	Construct a graph for the R-operator.
ParetoNBDRV.__init__([name, ndim_supp, ...])	Create a random variable Op.
ParetoNBDRV.add_tag_trace([user_line])	Add tag.trace to a node or variable.
ParetoNBDRV.batch_ndim(node)
ParetoNBDRV.dist_params(node)	Return the node inpust corresponding to dist params
ParetoNBDRV.do_constant_folding(fgraph, node)	Determine whether or not constant folding should be performed for the given node.
ParetoNBDRV.grad(inputs, outputs)	Construct a graph for the gradient with respect to each input variable.
ParetoNBDRV.infer_shape(fgraph, node, ...)
ParetoNBDRV.inplace_on_inputs(...)	Try to return a version of self that tries to inplace in as many as allowed_inplace_inputs.
ParetoNBDRV.make_node(rng, size, *dist_params)	Create a random variable node.
ParetoNBDRV.make_py_thunk(node, storage_map, ...)	Make a Python thunk.
ParetoNBDRV.make_thunk(node, storage_map, ...)	Create a thunk.
ParetoNBDRV.perform(node, inputs, outputs)	Calculate the function on the inputs and put the variables in the output storage.
ParetoNBDRV.prepare_node(node, storage_map, ...)	Make any special modifications that the Op needs before doing Op.make_thunk().
ParetoNBDRV.rng_fn(rng, r, alpha, s, beta, ...)	Sample a numeric random variate.
ParetoNBDRV.rng_param(node)	Return the node input corresponding to the rng
ParetoNBDRV.size_param(node)	Return the node input corresponding to the size

Attributes

default_output	An int that specifies which output Op.__call__() should return.
destroy_map	A dict that maps output indices to the input indices upon which they operate in-place.
dtype
itypes
name
otypes
signature
view_map	A dict that maps output indices to the input indices of which they are a view.