beliefs

A library for performing inference with Bayesian Networks for a special use case, derived from pgmpy.

Motivation

Exact inference

This library provides the ability to perform exact inference in a computationally tractable* way for a specific but useful case: Bayesian Networks with * polytree structure * consisting of Bernoulli random variables whose relationship to their parents in the probabilistic graphical model are described by AND or OR logic

Non-deterministic conditional probability distributions for multinomial, discrete random variables are also supported, although the algorithm is specifically optimized for the case of Bernoulli AND and Bernoulli OR variables.

*See the "Many parents model" in the jupyter notebook under the examples/ directory for an example of a case in which inference becomes computationally intractable with pgmpy but can be handled by beliefs optimized algorithm.

Additional features

In addition to being able to perform inference based on direct observation of a variable in the PGM, beliefs also provides the ability to specify virtual evidence, i.e. evidence that modifies the belief, or marginal probability, of a variable by affecting its likelihood based on observations of variables not in the PGM, while not pinning the variable into a definite (observed) state.
The ability to catch conflicting evidence errors during inference, which manifest as numpy NaNs in pgmpy's inference results.
Utility for gathering the direct observations that influenced the beliefs of variables that were inferred to be in a definite state.

Getting started

Installation

Using conda:

conda install -c driver beliefs

Example

Perform inference on a Bayesian Network:

from beliefs.inference.belief_propagation import BeliefPropagation
from beliefs.models.belief_update_node_model import (
    BeliefUpdateNodeModel,
    BernoulliOrNode
)

# directed edges for a polytree Bayes Net
edges = [('u', 'x'), ('v', 'x'), ('x', 'y'), ('x', 'z')

# initialize model w/ edges, default to OR CPD for all variables
model = BeliefUpdateNodeModel.init_from_edges(edges, BernoulliOrNode)

# initialize inference
infer = BeliefPropagation(model)

# perform inference, with 'x' is observed to be True.
result = infer.query(evidence={'x': np.array([0, 1])})

Tests

From the project root directory:

pytest tests -vv

License

This project is licensed under the terms of the MIT license.