🧠 Bayes MCP - Interactive Bayesian Analysis Suite

Interactive Bayesian analysis toolkit with hands-on notebooks for business decisions, medical diagnosis, and financial risk assessment. Learn and apply Bayesian methods through real-world scenarios.

🎯 What's Inside

📊 Interactive Notebooks (Start Here!)

• Overview & Fundamentals - Learn Bayesian concepts interactively
• A/B Testing Analysis - Business decision optimization
• Medical Diagnosis - Evidence-based clinical decisions
• Financial Risk Assessment - Portfolio management & VaR

🛠️ Core Engine

• Bayesian MCP Server - Production-ready statistical engine
• MCMC Sampling - Advanced computational methods
• Real-time Analysis - Interactive parameter exploration
• Visualization Tools - Uncertainty-aware plotting
• Logfire Integration - Observability and monitoring for Bayesian computations

🚀 Quick Start

1. Setup Environment

git clone <repository-url>
cd bayes
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -e .
pip install jupyter jupyterlab scipy matplotlib seaborn ipywidgets

2. Configure Logfire (Optional)

cp .env.example .env
# Edit .env and add your Logfire token for observability

3. Launch Interactive Notebooks

cd notebooks
jupyter lab
# Open 00_bayesian_analysis_overview.ipynb to start

4. Start with Fundamentals

• Coin Flip Demo - See Bayesian updating in action
• Choose Your Domain - A/B testing, medical, or financial
• Interactive Exploration - Adjust parameters, see real-time results

🎯 Use Cases & Applications

🧪 A/B Testing Excellence

• Direct Probability: Get P(B > A) instead of p-values
• Expected Loss: Quantify risk of wrong decisions
• Revenue Impact: Calculate business value of changes
• Early Stopping: Stop tests when confident enough

🏥 Medical Diagnosis Support

• Prior Integration: Use prevalence and risk factors
• Sequential Testing: Optimal diagnostic strategies
• Cost Analysis: Balance test costs vs. information gain
• Base Rate Awareness: Avoid common diagnostic errors

💰 Financial Risk Management

• Portfolio Optimization: Risk-adjusted returns
• Stress Testing: Scenario-based risk assessment
• VaR Calculation: Regulatory compliance metrics
• Market Regime: Dynamic risk model adjustment

🧮 Why Bayesian Methods?

Traditional Approach Problems:

• ❌ P-values don't answer business questions
• ❌ No uncertainty quantification
• ❌ Can't incorporate prior knowledge
• ❌ Fixed sample size requirements

Bayesian Solutions:

• ✅ Direct probability statements: "95% chance B is better"
• ✅ Full uncertainty distributions
• ✅ Prior knowledge integration
• ✅ Sequential analysis with optimal stopping

📚 Documentation

Complete Guides → docs/

• Project Overview - Vision and architecture
• Setup Guide - Detailed installation
• Implementation Plan - Technical details
• Contributing Guide - Development workflow

API Reference

• MCP Server: Start with python bayes_mcp.py --port 8002
• Engine Direct: from bayes_mcp.bayesian_engine import BayesianEngine
• REST API: Full HTTP interface for integration

🎓 Learning Path

Beginner 🌱

1. Bayesian Overview - Start here
2. Coin flip demo - Hands-on Bayes' theorem
3. A/B testing basics - Business applications
4. Parameter exploration - See how changes affect results

Intermediate 🌿

1. Medical diagnosis - Evidence combination
2. Financial risk - Portfolio optimization
3. Compare approaches - Bayesian vs. traditional
4. Custom scenarios - Adapt to your data

Advanced 🌳

1. Engine integration - Production applications
2. Model extension - Add complexity
3. API development - Build services
4. Contribution - Enhance the platform

🎪 Live Demonstrations

Run Comprehensive Demos

# All domains demonstration
python demos/master_demo.py

# Individual demos
python demos/ab_testing_demo.py      # Business optimization
python demos/medical_diagnosis_demo.py # Clinical decisions
python demos/financial_risk_demo.py   # Portfolio management

🎯 Success Metrics

Educational Impact

• Interactive Learning: Hands-on Bayesian education
• Real Scenarios: Practical business applications
• Visual Feedback: Immediate understanding
• Progressive Complexity: Learn at your pace

Business Value

• Better Decisions: Uncertainty-aware choices
• Risk Management: Quantified downside protection
• Cost Optimization: Efficient resource allocation
• Competitive Advantage: Advanced analytical capabilities

📊 Observability with Logfire

When configured with a Logfire token, the Bayes MCP server provides comprehensive observability:

Performance Metrics

• MCMC Sampling Time - Track computational performance
• Model Creation - Monitor model initialization
• Variable Counts - Track model complexity
• API Response Times - Service health monitoring

Tracing Features

• Request Tracing - Full request lifecycle visibility
• Span Tracking - Detailed operation timing
• Error Tracking - Exception monitoring with context
• Structured Logging - Rich contextual information

Integration Benefits

• Performance Optimization - Identify bottlenecks
• Debugging Support - Trace complex computations
• Usage Analytics - Understand model patterns
• Production Monitoring - Ensure reliability

🔧 Technical Stack

• Python 3.8+ - Core language
• Jupyter Lab - Interactive interface
• PyMC - Bayesian computation engine
• NumPy/SciPy - Numerical computing
• Matplotlib/Seaborn - Visualization
• IPyWidgets - Interactive controls
• FastAPI - Production server (optional)

🏃‍♂️ Running Tests

# Full test suite (36 tests)
python -m pytest tests/ -v

# Quick functionality check
python -c "from bayes_mcp.bayesian_engine import BayesianEngine; print('✅ Engine ready!')"

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Test your changes: python -m pytest
4. Commit changes: git commit -m 'Add amazing feature'
5. Push branch: git push origin feature/amazing-feature
6. Open Pull Request

📄 License

MIT License - see LICENSE for details.

🎉 Get Started Today!

Ready to explore Bayesian analysis?

# Launch the interactive suite
cd bayes/notebooks
jupyter lab
# Open 00_bayesian_analysis_overview.ipynb

Transform your decision-making with principled uncertainty quantification! 🚀

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Built for practitioners who need reliable, interpretable, and actionable statistical analysis.