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Bayesian MCP

by wrenchchatrepo
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Python
Hybrid
MIT License
2

Bayesian MCP

A Model Calling Protocol (MCP) server for Bayesian reasoning, inference, and belief updating. This tool enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning.

Features

  • 🧠 Bayesian Inference: Update beliefs with new evidence using MCMC sampling

  • 📊 Model Comparison: Compare competing models using information criteria

  • 🔮 Predictive Inference: Generate predictions with uncertainty quantification

  • 📈 Visualization: Create visualizations of posterior distributions

  • 🔌 MCP Integration: Seamlessly integrate with any LLM that supports MCP

Related MCP server: Calculator MCP Server

Installation

Development Installation

Clone the repository and install dependencies:

git clone https://github.com/wrenchchatrepo/bayesian-mcp.git cd bayesian-mcp pip install -e .

Requirements

  • Python 3.9+

  • PyMC 5.0+

  • ArviZ

  • NumPy

  • Matplotlib

  • FastAPI

  • Uvicorn

Quick Start

Starting the Server

# Run with default settings python bayesian_mcp.py # Specify host and port python bayesian_mcp.py --host 0.0.0.0 --port 8080 # Set log level python bayesian_mcp.py --log-level debug

The server will start and listen for MCP requests on the specified host and port.

API Usage

The Bayesian MCP server exposes several functions through its API:

1. Create Model

Create a new Bayesian model with specified variables.

# MCP Request { "function_name": "create_model", "parameters": { "model_name": "my_model", "variables": { "theta": { "distribution": "normal", "params": {"mu": 0, "sigma": 1} }, "likelihood": { "distribution": "normal", "params": {"mu": "theta", "sigma": 0.5}, "observed": [0.1, 0.2, 0.3, 0.4] } } } }

2. Update Beliefs

Update model beliefs with new evidence.

# MCP Request { "function_name": "update_beliefs", "parameters": { "model_name": "my_model", "evidence": { "data": [0.1, 0.2, 0.3, 0.4] }, "sample_kwargs": { "draws": 1000, "tune": 1000, "chains": 2 } } }

3. Make Predictions

Generate predictions using the posterior distribution.

# MCP Request { "function_name": "predict", "parameters": { "model_name": "my_model", "variables": ["theta"], "conditions": { "x": [1.0, 2.0, 3.0] } } }

4. Compare Models

Compare multiple models using information criteria.

# MCP Request { "function_name": "compare_models", "parameters": { "model_names": ["model_1", "model_2"], "metric": "waic" } }

5. Create Visualization

Generate visualizations of model posterior distributions.

# MCP Request { "function_name": "create_visualization", "parameters": { "model_name": "my_model", "plot_type": "trace", "variables": ["theta"] } }

Examples

The examples/ directory contains several examples demonstrating how to use the Bayesian MCP server:

Linear Regression

A simple linear regression example to demonstrate parameter estimation:

python examples/linear_regression.py

A/B Testing

An example of Bayesian A/B testing for conversion rates:

python examples/ab_test.py

Supported Distributions

The Bayesian engine supports the following distributions:

  • normal: Normal (Gaussian) distribution

  • lognormal: Log-normal distribution

  • beta: Beta distribution

  • gamma: Gamma distribution

  • exponential: Exponential distribution

  • uniform: Uniform distribution

  • bernoulli: Bernoulli distribution

  • binomial: Binomial distribution

  • poisson: Poisson distribution

  • deterministic: Deterministic transformation

MCP Integration

This server implements the Model Calling Protocol, making it compatible with a wide range of LLMs and frameworks. To use it with your LLM:

import requests response = requests.post("http://localhost:8000/mcp", json={ "function_name": "create_model", "parameters": { "model_name": "example_model", "variables": {...} } }) result = response.json()

License

MIT

Credits

Based on concepts and code from the Wrench AI framework.

This server cannot be installed

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security - not tested
A
license - permissive license
-
quality - not tested

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