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bayesflow-simulator

Creates Bee 2.0-compliant simulators with specified data-generation models.

Updated Mar 5, 2026
Search Tags:#data generation#simulator#statistical modeling#bayesflow#prior function#likelihood function
One-click install
npx skills add https://github.com/matthiaskloft/claude-skills --skill bayesflow-simulator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bayesflow-simulator
Source: https://github.com/matthiaskloft/claude-skills/tree/main/bayesflow/skills/bayesflow-simulator
Command: npx skills add https://github.com/matthiaskloft/claude-skills --skill bayesflow-simulator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill assists in the creation, modification, and debugging of data-generating processes (simulators) for the BayesFlow 2.x library, ensuring correct implementation of prior, likelihood, and meta functions.

Core Features & Use Cases

  • Simulator Factory: Provides patterns for both subclassing Simulator (for variable-size data) and using the make_simulator() function (for fixed-size data).
  • Convention Enforcement: Guides users on critical aspects like function signatures, RNG discipline, output dictionary conventions, and configuration practices.
  • Use Case: You need to build a new simulator for a complex statistical model. This Skill will guide you through defining the prior distributions, the likelihood function, and any necessary meta-parameters, ensuring the simulator integrates seamlessly with BayesFlow.

Quick Start

Use the bayesflow-simulator skill to create a new simulator using the make_simulator function with provided prior, likelihood, and meta functions.

Frequently Asked Questions about bayesflow-simulator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a simulator for BayesFlow 2.x with custom prior and likelihood functions?▼

To create a BayesFlow 2.x simulator, define your prior, likelihood, and meta functions, then assemble them using the make_simulator function for fixed-size data or by subclassing Simulator for variable-size data.

What is the correct output format for a BayesFlow simulator function?▼

A BayesFlow simulator function must return an output dictionary adhering to specific BayesFlow 2.x conventions, ensuring the generated data integrates correctly with the library's statistical modeling pipeline.

How do I manage the random number generator when building a BayesFlow simulator?▼

When building a BayesFlow simulator, you must follow strict RNG discipline by correctly passing and handling the random number generator within your prior and likelihood function signatures.

When should I subclass Simulator instead of using make_simulator in BayesFlow?▼

You should subclass Simulator when generating variable-sized data, whereas using the make_simulator function is appropriate for creating simulators that handle fixed-sized data in BayesFlow 2.x.

What are the required function signatures for BayesFlow 2.x simulator components?▼

BayesFlow 2.x simulator components require strict function signatures for prior, likelihood, and meta functions to ensure proper configuration and execution of the data-generating process.

Why does my BayesFlow simulator fail during statistical modeling integration?▼

Your BayesFlow simulator likely fails due to incorrect function signatures, improper RNG discipline, or output dictionary conventions not meeting the critical standards required by the BayesFlow 2.x library.

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