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probability-inference

Estimate parameters and validate residual structure with reproducible calibration settings.

Updated Feb 10, 2026
Search Tags:#diagnostics#probability#modeling#inference#risk#quantitative
One-click install
npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill probability-inference
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: probability-inference
Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/probability-inference
Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill probability-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need for robust probability inference in quantitative research, ensuring that models accurately reflect distribution assumptions and account for tail-event realism.

Core Features & Use Cases

  • Reproducible Research: Ensures that probability inference workflows are executed with reproducible research practices.
  • Parameter Estimation & Validation: Facilitates the estimation of parameters with reproducible calibration settings and thorough validation of model behavior.
  • Risk Control: Implements essential risk controls, including parameter bounds, convergence safeguards, and monitoring for drift.
  • Use Case: A quantitative analyst needs to build a new risk model that accounts for extreme market events. This Skill can be used to define the model's assumptions, estimate parameters, validate its stability, and ensure it meets strict release criteria.

Quick Start

Use the probability-inference skill to run diagnostics on the input data file 'market_data.csv'.

Frequently Asked Questions about probability-inference

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

FAQPage Schema
How do I estimate parameters for a quantitative risk model with extreme tail events?▼

Probability inference estimates parameters for quantitative risk models by enforcing distribution assumptions and tail-event realism. It uses reproducible calibration settings to ensure models accurately reflect extreme market events.

What is probability inference for tail-event realism in quantitative research?▼

Probability inference is a workflow that validates distribution assumptions to account for extreme market events. It ensures robust quantitative research by validating residual structure and enforcing risk controls.

How do I validate residual structure and monitor for drift in probabilistic modeling?▼

You validate residual structure and monitor for drift in probabilistic modeling by executing probability inference workflows with reproducible calibration settings. This enforces parameter bounds and convergence safeguards for model stability.

Can I run diagnostics on a market data CSV file for probability inference?▼

Yes, you can run diagnostics on an input data file like 'market_data.csv' using probability inference workflows. This process estimates parameters, validates model behavior, and enforces risk controls for stability.

What risk controls do I need for stable probability inference and parameter estimation?▼

Risk controls for stable probability inference include parameter bounds, convergence safeguards, and drift monitoring. These mechanisms ensure stability and accuracy when estimating parameters with reproducible calibration settings.

When should I not use standard distribution assumptions for quantitative risk modeling?▼

You should avoid standard distribution assumptions when your quantitative risk model must account for extreme market tail events. Instead, use probability inference workflows that enforce tail-event realism and validate residual structure.

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