Bayesian Cognitive Model Builder
CommunityGuides hierarchical Bayesian cognitive modeling.
Education & Research#bayesian#stan#pymc#priors#cognitive-modeling#mcmc-diagnostics#posterior-predictive-checks
AuthorHaoxuanLiTHUAI
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides domain-validated guidance for building hierarchical Bayesian cognitive models using probabilistic programming languages (Stan, PyMC). It covers priors that respect cognitive constraints, when to use hierarchical structure, how to diagnose MCMC pathologies, and how to evaluate model adequacy through posterior predictive checks.
Core Features & Use Cases
- Hierarchical modeling guidance: priors selection, centering vs non-centering, and regularization across participants
- MCMC diagnostics & model checking: convergence diagnostics, trace and rank plots, posterior predictive checks
- Model comparison & reporting: information criteria (PSIS-LOO/WAIC), Bayes factors, sensitivity analyses, and reproducible workflows
Quick Start
Set up your first hierarchical Bayesian model using Stan or PyMC and run the diagnostic workflow.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
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Please help me install this Skill: Name: Bayesian Cognitive Model Builder Download link: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/archive/main.zip#bayesian-cognitive-model-builder Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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