README.md•4.43 kB
# 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:
```bash
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
```bash
# 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.
```python
# 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.
```python
# 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.
```python
# 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.
```python
# MCP Request
{
"function_name": "compare_models",
"parameters": {
"model_names": ["model_1", "model_2"],
"metric": "waic"
}
}
```
### 5. Create Visualization
Generate visualizations of model posterior distributions.
```python
# 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:
```bash
python examples/linear_regression.py
```
### A/B Testing
An example of Bayesian A/B testing for conversion rates:
```bash
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:
```python
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.