Bayesianisches MCP

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

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

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Ein Model Calling Protocol-Server, der es LLMs ermöglicht, strenge Bayes-Analysen und probabilistische Schlussfolgerungen durchzuführen, einschließlich Inferenz, Modellvergleich und prädiktiver Modellierung mit Unsicherheitsquantifizierung.

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Bayesian MCP

Bayesian MCP

Features

Installation

Development Installation

Requirements

Quick Start

Starting the Server

API Usage

1. Create Model

2. Update Beliefs

3. Make Predictions

4. Compare Models

5. Create Visualization

Examples

Linear Regression

A/B Testing

Supported Distributions

MCP Integration

License

Credits

Related MCP Servers

Math-MCP

Calculator MCP Server

MCP Database Server

MCP Orchestrator

New MCP Servers

MCP directory API