Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
PyMC Bayesian Modeler
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
instructions.md
--- name: pymc-bayesian-modeler-46c03def594f description: PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: physics domain: science category: data-analysis phase: 6 graph: domains: [domain:physics] skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis] workflows: [workflow:experiment-design, workflow:peer-review-cycle] roles: [role:research-scientist, role:computational-scientist] --- # PyMC Bayesian Modeler ## Purpose Provides expert guidance on PyMC for Bayesian modeling in physics, including hierarchical models and advanced inference methods. ## Capabilities - Probabilistic model construction - NUTS/HMC sampling - Variational inference - Gaussian processes - Model comparison (WAIC, LOO) - Prior predictive checks ## Usage Guidelines 1. **Model Building**: Construct probabilistic models 2. **Priors**: Specify informative or weakly informative priors 3. **Sampling**: Use NUTS for efficient sampling 4. **Diagnostics**: Check convergence with trace plots and r-hat 5. **Comparison**: Compare models with information criteria ## Tools/Libraries - PyMC - arviz - Theano/JAX--- name: pymc-bayesian-modeler-46c03def594f description: PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: physics domain: science category: data-analysis phase: 6 graph: domains: [domain:physics] skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis] workflows: [workflow:experiment-design, workflow:peer-review-cycle] roles: [role:research-scientist, role:computational-scientist] --- # PyMC Bayesian Modeler ## Purpose Provides expert guidance on PyMC for Bayesian modeling in physics, including hierarchical models and advanced inference methods. ## Capabilities - Probabilistic model construction - NUTS/HMC sampling - Variational inference - Gaussian processes - Model comparison (WAIC, LOO) - Prior predictive checks ## Usage Guidelines 1. **Model Building**: Construct probabilistic models 2. **Priors**: Specify informative or weakly informative priors 3. **Sampling**: Use NUTS for efficient sampling 4. **Diagnostics**: Check convergence with trace plots and r-hat 5. **Comparison**: Compare models with information criteria ## Tools/Libraries - PyMC - arviz - Theano/JAX
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.