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Designing Graphical Causal Bayesian Networks in Python

Query the Bayesian Network

Explore how to perform and analyze queries on Bayesian networks to assess project maturity levels and their influence on risk factors like project drift and overcost. This lesson helps you interpret output visualizations for deeper insights into model performance and real-world applicability.

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Our primary focus in this new lesson is to test various queries using our well-structured model. This essential step will help us evaluate the performance of our model and ensure its reliability. Moreover, we analyze the outputs, gaining valuable insights into the model's effectiveness and understanding how it can be used for real-world applications.

Query evidence

For the upcoming exercise, your task is to modify the provided code to perform a query on the model, given that all maturity level nodes have evidence of Maturity = Level 1. This means that the project management maturity is not following the best practices of the industry.

Complete the code accordingly to obtain the desired results for this specific scenario.

Python 3.8
# Call the InferenceEngine:
ie = 
# Set the evidence for all Maturity nodes
evidence = 
# Perform the query with the provided evidence
evidence_Test = 
rounded_evidence_Test = {
    outer_key: {inner_key: round(value, 1) for inner_key, value in inner_dict.items()}
    for outer_key, inner_dict in evidence_Test.items()}
def test(n):
  return rounded_evidence_Test

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