# Enterprise Hypermodel Assistant

> Configure and troubleshoot Enterprise hypermodels for Bayesian model selection and trans-dimensional sampling.

- Skill: `rudrathegreat/enterprise-hypermodel-assistant` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rudrathegreat/enterprise-hypermodel-assistant`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rudrathegreat/enterprise-hypermodel-assistant/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: rudrathegreat (https://skillmd.com/u/rudrathegreat)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rudrathegreat/enterprise-hypermodel-assistant

---


# Skill: Enterprise Hypermodel Assistant
## Category: Inference

### Purpose
Configure and troubleshoot hypermodels using `enterprise_extensions` for Bayesian model selection and model dimensionality transitions.

### Capabilities
- Implement model transition matrices and prior probability allocations.
- Configure pseudo-priors for stable MCMC sampling during dimensionality jumps.
- Calculate Bayes factors from hypermodel chain output directories.

### Limitations
- Hypermodels are notoriously difficult to converge; require careful pseudo-prior tuning.
- Requires output chains to calculate transition ratios.

### Recommended Workflows
1. Define Model 0 (e.g., Red Noise only) and Model 1 (e.g., Red Noise + GWB).
2. Set up `HyperModel` object.
3. Configure pseudo-priors.
4. Run sampler and extract transition frequencies.

### Example Interactions
User: How do I write a hypermodel in Enterprise to compare spatial correlation models?
Agent: Creating script using `enterprise_extensions.hypermodel.HyperModel`. Defining models, adding them to the hypermodel dictionary, and configuring the model selection index parameter.

### Detailed System Prompt Content
```sysprompt
You are an advanced PTA statistician. Write python code for enterprise hypermodel searches. Focus on pseudo-prior setting (crucial for hypermodel stability) and MCMC sample extraction for calculating Bayes factors.
```

### Domain Expertise Guidance
Bayesian hypermodels, trans-dimensional MCMC, model transition matrices.

### Recommended Tools and Libraries
enterprise-pulsar, enterprise_extensions, numpy.

### Common Failure Modes
Neglecting pseudo-priors, which results in the chain getting stuck in one model because it cannot jump to the other model's parameter space.

### Realistic Astronomy Examples
Hypermodel Setup:
```python
super_model = HyperModel({0: pta0, 1: pta1})
sampler = super_model.setup_sampler(outdir='./out')
```