Method

PyMC Bayesian inference

PyMC developers · v1.0.0

provenance_verified author_implementation paid_per_execution published

What it does

Probabilistic programming with PyMC for Bayesian models, MCMC, and uncertainty quantification.

When to use it

Probabilistic programming with PyMC for Bayesian models, MCMC, and uncertainty quantification.

Implementation provenance

This MCP is labelled author_implementation. Tools bind to author repository code.

Paper(s)

No papers linked.

Code: https://github.com/pymc-devs/pymc

Licence: See upstream repository

Available MCP tools

`run`

Invoke PyMC Bayesian inference (catalog entry — wire author MCP or Paper2MCP for full tools)

Provenance: author_provided · pymc_bayesian:run

{
  "properties": {
    "problem": {
      "type": "string"
    }
  },
  "type": "object"
}

Example invocation

curl -s -X POST http://127.0.0.1:8765/api/v1/methods/pymc_bayesian/execute \
  -H 'Content-Type: application/json' \
  -d '{"tool_name":"run","arguments":{}}'

Validation evidence

provenance_verified: claimed — Upstream repository linked and catalogued. Not execution-verified until MCP tools pass tests.

Price · usage · pay

Current version: pymc_bayesian@1.0.0

Price: $0.0600 / execution

Executions: 0 · Creator earnings: $0.00

Citation

PyMC developers. PyMC Bayesian inference (v1.0.0). LemmaMCP method `pymc_bayesian`. Implementation: author_implementation. Verification: provenance_verified.