Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
PyMC Probabilistic Programming
PyMC for flexible Bayesian modeling
instructions.md
--- name: pymc-probabilistic-programming-6466801968cb description: PyMC for flexible Bayesian modeling allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: mathematics domain: science category: statistical-computing phase: 6 graph: domains: [domain:mathematics] specializations: [specialization:computational-mathematics] skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis] workflows: [workflow:experiment-design] roles: [role:research-scientist, role:data-scientist] --- # PyMC Probabilistic Programming ## Purpose Provides PyMC capabilities for flexible Bayesian modeling and probabilistic programming in Python. ## Capabilities - Hierarchical model specification - Custom distributions - Gaussian processes - MCMC and variational inference - Model diagnostics - ArviZ integration for visualization ## Usage Guidelines 1. **Model Building**: Use PyMC context managers 2. **Custom Distributions**: Define distributions when needed 3. **Hierarchical Models**: Build proper hierarchical structures 4. **Visualization**: Use ArviZ for diagnostic plots ## Tools/Libraries - PyMC - ArviZ - Theano/PyTensor--- name: pymc-probabilistic-programming-6466801968cb description: PyMC for flexible Bayesian modeling allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: mathematics domain: science category: statistical-computing phase: 6 graph: domains: [domain:mathematics] specializations: [specialization:computational-mathematics] skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis] workflows: [workflow:experiment-design] roles: [role:research-scientist, role:data-scientist] --- # PyMC Probabilistic Programming ## Purpose Provides PyMC capabilities for flexible Bayesian modeling and probabilistic programming in Python. ## Capabilities - Hierarchical model specification - Custom distributions - Gaussian processes - MCMC and variational inference - Model diagnostics - ArviZ integration for visualization ## Usage Guidelines 1. **Model Building**: Use PyMC context managers 2. **Custom Distributions**: Define distributions when needed 3. **Hierarchical Models**: Build proper hierarchical structures 4. **Visualization**: Use ArviZ for diagnostic plots ## Tools/Libraries - PyMC - ArviZ - Theano/PyTensor
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.