bayesian-inference-engine
Bayesian probabilistic reasoning for prior specification, posterior computation, and belief updating
Best use case
bayesian-inference-engine is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Bayesian probabilistic reasoning for prior specification, posterior computation, and belief updating
Teams using bayesian-inference-engine should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bayesian-inference-engine/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bayesian-inference-engine Compares
| Feature / Agent | bayesian-inference-engine | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Bayesian probabilistic reasoning for prior specification, posterior computation, and belief updating
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Bayesian Inference Engine ## Purpose Provides Bayesian probabilistic reasoning capabilities for prior specification, posterior computation, and sequential belief updating. ## Capabilities - Prior elicitation support - MCMC sampling (NUTS, HMC) - Variational inference - Model comparison (Bayes factors, LOO-CV) - Posterior predictive checking - Sequential belief updating ## Usage Guidelines 1. **Prior Selection**: Choose appropriate, defensible priors 2. **Sampling**: Use efficient MCMC algorithms 3. **Diagnostics**: Check convergence and mixing 4. **Model Comparison**: Use appropriate comparison criteria ## Tools/Libraries - PyMC - Stan (PyStan) - ArviZ - NumPyro
Related Skills
Ghidra/IDA Reverse Engineering Skill
Deep integration with Ghidra and IDA Pro for binary analysis and reverse engineering
physics-engine
Physics engine integration skill for rigid body dynamics and collision detection.
causal-inference-methods
Apply propensity score methods, instrumental variables, difference-in-differences, and regression discontinuity designs for causal identification
music-prompt-engineering
Optimize and format prompts specifically for AI music generation platforms like Suno and Udio, including platform-specific syntax and tag optimization
video-prompt-engineering
Optimize prompts for AI video generation platforms including Sora, Runway, Pika, and Kling
meta-analysis-engine
Skill for conducting meta-analyses of research findings
pymc-bayesian-modeler
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
stan-bayesian-modeling
Stan probabilistic programming for Bayesian inference
type-inference-engine
Implement and test type inference algorithms including Algorithm W and constraint-based inference
traffic-simulation-engine
Traffic simulation skill for microsimulation, level of service, and signal optimization
hydrologic-modeling-engine
Hydrologic modeling skill for rainfall-runoff analysis, flood frequency, and watershed analysis
hydraulic-analysis-engine
Hydraulic analysis skill for open channel flow, culverts, and pipe networks