enterprise-hypermodel-assistantlisted

# 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 matric