bayesian-network-analyzer

# Bayesian Network Analyzer ## Overview The Bayesian Network Analyzer skill provides comprehensive capabilities for constructing, analyzing, and reasoning with Bayesian networks. It enables probabilistic inference, causal effect estimation, and belief updating based on new evidence, supporting data-driven decision-making under uncertainty. ## Capabilities - DAG structure learning from data - Conditional probability table estimation - Belief propagation and inference - Causal effect estimation - Sensitivity to evidence analysis - What-if scenario evaluation - Network visualization - Integration with external data sources ## Used By Processes - Structured Decision Making Process - Predictive Analytics Implementation - Decision Quality Assessment - Cognitive Bias Debiasing Process ## Usage ### Network Structure Definition ```python # Define network structure network_structure = { "nodes": [ {"name": "MarketCondition", "states": ["Favorable", "Unfavorable"]}, {"name": "CompetitorAction", "states": ["Aggressive", "Passive"]}, {"name": "ProductSuccess", "states": ["High", "Medium", "Low"]} ], "edges": [ {"from": "MarketCondition", "to": "ProductSuccess"}, {"from": "CompetitorAction", "to": "ProductSuccess"} ] } ``` ### Conditional Probability Tables ```python # Define CPTs cpts = { "MarketCondition": {"Favorable": 0.6, "Unfavorable": 0.4}, "CompetitorAction": {"Aggressive": 0.3, "Passive": 0.7}, "Product...