SymbolicBayesNet - GTSAM Docs

Authors: Frank Dellaert, et al. (see THANKS for the full author list)

See LICENSE for the license information

A SymbolicBayesNet is a directed acyclic graph (DAG) composed of SymbolicConditional objects. It represents the structure of a factorized probability distribution P(X) = Π P(Xi | Parents(Xi)) purely in terms of variable connectivity.

It is typically the result of running sequential variable elimination on a SymbolicFactorGraph.

%pip install --quiet gtsam-develop

Note: you may need to restart the kernel to use updated packages.

from gtsam import SymbolicConditional, SymbolicFactorGraph, Ordering
from gtsam.symbol_shorthand import X, L
import graphviz

Creating a SymbolicBayesNet¶

SymbolicBayesNets are usually created by eliminating a SymbolicFactorGraph. But you can also build them directly:

from gtsam import SymbolicBayesNet

# Create a new Bayes Net
symbolic_bayes_net = SymbolicBayesNet()

# Add conditionals directly
symbolic_bayes_net.push_back(SymbolicConditional(L(1), X(0)))  # P(l1 | x0)
symbolic_bayes_net.push_back(SymbolicConditional(X(0), X(1)))  # P(x0 | x1)
symbolic_bayes_net.push_back(SymbolicConditional(L(2), X(1)))  # P(l2 | x1)
symbolic_bayes_net.push_back(SymbolicConditional(X(1), X(2)))  # P(x1 | x2)
symbolic_bayes_net.push_back(SymbolicConditional(X(2)))  # P(x2)

symbolic_bayes_net.print("Directly Built Symbolic Bayes Net:\n")

Directly Built Symbolic Bayes Net:
 
size: 5
conditional 0:  P( l1 | x0)
conditional 1:  P( x0 | x1)
conditional 2:  P( l2 | x1)
conditional 3:  P( x1 | x2)
conditional 4:  P( x2)

Accessing Conditionals and Visualization¶

# Access a conditional by index
conditional_1 = bayes_net.at(1) # P(x0 | l1)
conditional_1.print("Conditional at index 1: ")

# Visualize the Bayes Net structure
display(graphviz.Source(bayes_net.dot()))

Conditional at index 1:  P( x0 | x1)

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