---
name: bayesian-network-analyzer-1efad0357386
description: Bayesian network construction and inference skill for probabilistic reasoning, causal analysis, and belief updating
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: decision-intelligence
  domain: business
  category: quantitative-analysis
  priority: medium
  tools-libraries:
    - pgmpy
    - pomegranate
    - bnlearn
    - pyAgrum
graph:
  domains: [domain:business-intelligence]
  skillAreas: [skill-area:statistical-analysis, skill-area:data-analysis, skill-area:quantitative-modeling]
  roles: [role:data-scientist, role:data-analyst, role:research-scientist]
---

# 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},
    "ProductSuccess": {
        # P(Success | Market, Competitor)
        ("Favorable", "Passive"): {"High": 0.7, "Medium": 0.2, "Low": 0.1},
        ("Favorable", "Aggressive"): {"High": 0.4, "Medium": 0.4, "Low": 0.2},
        ("Unfavorable", "Passive"): {"High": 0.3, "Medium": 0.4, "Low": 0.3},
        ("Unfavorable", "Aggressive"): {"High": 0.1, "Medium": 0.3, "Low": 0.6}
    }
}
```

### Inference Queries

Supported inference types:
- **Marginal probability**: P(ProductSuccess = High)
- **Conditional probability**: P(ProductSuccess = High | MarketCondition = Favorable)
- **Most probable explanation**: argmax P(all variables | evidence)
- **Maximum a posteriori**: argmax P(query | evidence)

### Structure Learning

Learn network structure from data using:
- Constraint-based methods (PC algorithm, FCI)
- Score-based methods (Hill Climbing, K2)
- Hybrid methods (MMHC)

### Causal Analysis

- Identify causal vs. correlational relationships
- Compute causal effects using do-calculus
- Analyze confounding and mediation

## Input Schema

```json
{
  "network": {
    "nodes": ["object"],
    "edges": ["object"],
    "cpts": "object"
  },
  "query": {
    "type": "marginal|conditional|mpe|map",
    "target_variables": ["string"],
    "evidence": "object"
  },
  "options": {
    "inference_algorithm": "variable_elimination|belief_propagation|sampling",
    "structure_learning": "boolean",
    "visualize": "boolean"
  }
}
```

## Output Schema

```json
{
  "query_result": {
    "probabilities": "object",
    "most_likely_state": "string",
    "confidence": "number"
  },
  "causal_effects": "object",
  "sensitivity": {
    "influential_parameters": ["string"],
    "robustness_score": "number"
  },
  "visualization_path": "string"
}
```

## Best Practices

1. Validate DAG structure for acyclicity
2. Ensure CPT probabilities sum to 1.0 for each parent configuration
3. Use domain expertise to guide structure learning
4. Validate learned structures against known causal relationships
5. Perform sensitivity analysis on uncertain probability estimates
6. Document assumptions behind probability assessments

## Integration Points

- Connects with Decision Tree Builder for integrated decision analysis
- Supports Monte Carlo Engine for sampling-based inference
- Feeds into Decision Visualization for network diagrams
- Integrates with Causal Inference Engine for advanced causal analysis


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing, Product Management, Sales
Roles: Business Development, Data Analyst, Data Scientist, Marketing Analyst, Product Manager
Specialties: API
Tools: Bnlearn, Pgmpy, Pomegranate, Pyagrum
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 38f6f7b65c764e8de3c683de5f2d27d0ee1f23068aba3cd5ff5e6c983ebeda7b