Model

class liesel.model.model.Model(nodes_and_vars, grow=True, copy=False)

Bases: object

A model with a static graph.

Parameters:

• nodes_and_vars (Iterable[Node | Var]) – The nodes and variables to include in the model.

• grow (bool) – Whether a GraphBuilder should be used to grow the model (finding the recursive inputs of the nodes and variables), and to add the model nodes. (default: True)

• copy (bool) – Whether the nodes and variables should be copied upon initialization. (default: False)

Methods

copy_nodes_and_vars()	Returns an unfrozen deep copy of the model nodes and variables.
groups()	Collects the groups from all nodes and variables.
pop_nodes_and_vars()	Pops the nodes and variables out of this model.
set_seed(seed)	Splits and sets the seed / PRNG key.
simulate(seed[, skip])	Updates the model state simulating from the probability distributions in the model using a provided random seed, optionally skipping specified nodes.
update(*names)	Updates the target nodes and their recursive inputs if they are outdated.

Attributes

auto_update	Whether to update the model automatically if the value of a node is modified.
log_lik	The log-likelihood of the model.
log_prior	The log-prior of the model.
log_prob	The (unnormalized) log-probability / log-posterior of the model.
node_graph	The directed graph of the model nodes.
nodes	A mapping of the model nodes with their names as keys.
state	The state of the model as a dict of node names and states.
var_graph	The directed graph of the model variables.
vars	A mapping of the model variables with their names as keys.