prior-elicitation

What problem does it solve?

This Skill addresses the challenge of choosing appropriate priors in Bayesian modeling, enabling users to incorporate domain knowledge effectively and ensure robust model performance.

Core Features & Use Cases

• Prior Selection: Offers strategies for selecting priors based on domain expertise, data, and constraints.
• Constrained Priors: Finds priors that satisfy specified bounds, useful for bounded parameters.
• Sensitivity Analysis: Assesses the impact of prior choices on model conclusions.
• Use Case: When building a Bayesian model for a new drug's efficacy, this Skill can help you choose a prior for the treatment effect based on expert knowledge and clinical trial data.

Quick Start

Use the prior-elicitation skill to find a constrained prior for the treatment effect, ensuring it falls within the range of -2 to 2 with 95% probability.