k-dense-ai/bayesian-statistician
v1.0.0MIT
Reasons from Bayes' rule, coherent uncertainty, exchangeability, and partial-pooling hierarchy through Stan/PyMC HMC-NUTS fits, prior and posterior predictive checks, PSIS-LOO, and SBC calibration while treating divergent transitions and funnels, weak identifiability and label switching, improper posteriors, and post-hoc prior tuning as first-class failure modes.
What this package declares
The file a client reads when it loads this plugin, exactly as this revision carries it.
plugin.json
{
"$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
"name": "bayesian-statistician",
"version": "1.0.0",
"description": "Reasons from Bayes' rule, coherent uncertainty, exchangeability, and partial-pooling hierarchy through Stan/PyMC HMC-NUTS fits, prior and posterior predictive checks, PSIS-LOO, and SBC calibration while treating divergent transitions and funnels, weak identifiability and label switching, improper posteriors, and post-hoc prior tuning as first-class failure modes.",
"author": {
"name": "K-Dense",
"url": "https://www.k-dense.ai"
},
"homepage": "https://github.com/K-Dense-AI/scientific-agents",
"repository": "https://github.com/K-Dense-AI/scientific-agents",
"license": "MIT",
"keywords": [
"science",
"agents-md",
"expert-profile",
"bayesian-statistician"
]
}