Node

class liesel.model.nodes.Node(*inputs, _name='', _needs_seed=False, **kwinputs)

Bases: ABC

A node of a computational graph that can cache its value.

Liesel represents statistical models as directed acyclic graphs (DAGs) of random variables (see Var) and computational nodes. The graph of random variables is built on top of the computational graph. The nodes of the computational graph will typically express computations in JAX returning arrays or pytrees, but in general, they can represent arbitrary operations in Python.

Nodes can cache the result of the operations they represent, improving the efficiency of the graph. The cached values are part of the model state (see Model.state), and can be stored in a chain by Liesel’s MCMC engine Goose.

This class is an abstract class that cannot be initialized without defining the update() method. See below for the most important concrete node classes.

Additional meta-information can be added by wrapping a Data or Calc node in a Var, which is particularly useful if the node represents a random variable, but can also be a good idea in a few other situations.

See also

Calc

A node representing a general calculation/operation in JAX or Python.

Data

A node representing some static data.

Dist

A node representing a TensorFlow Probability distribution, typically for the evaluation of the log-probability.

Methods

all_input_nodes()	Returns all non-keyword and keyword input nodes as a unique tuple.
all_output_nodes()	Returns all output nodes as a unique tuple.
clear_state()	Clears the state of the node.
flag_outdated()	Flags the node and its recursive outputs as outdated.
set_inputs(*inputs, **kwinputs)	Sets the non-keyword and keyword input nodes.
update()	Updates the value of the node.

Attributes

groups	The groups that this node is a part of.
inputs	The non-keyword input nodes.
kwinputs	The keyword input nodes.
model	The model the node is part of.
name	The name of the node.
needs_seed	Whether the node needs a seed / PRNG key.
outdated	Whether the node is outdated.
outputs	The output nodes.
state	The state of the node.
value	The value of the node.
var	The variable the node is part of.
monitor	Whether the node should be monitored by an inference algorithm.