bayesian-reasoning

Name: bayesian-reasoning
Availability: InStock
Author: jacob-balslev

Update posterior confidence from priors and evidence strength under uncertainty.

1|Updated May 6, 2026

Search Tags:#calibration #uncertainty #forecasting #base rate #bayesian reasoning #belief update #likelihood ratio

Ready to run

npx skills add https://github.com/jacob-balslev/skill-graph --skill bayesian-reasoning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bayesian-reasoning
Source: https://github.com/jacob-balslev/skill-graph/tree/main/marketplace/skills/bayesian-reasoning
Command: npx skills add https://github.com/jacob-balslev/skill-graph --skill bayesian-reasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of how to update a decision-relevant belief under uncertainty without overreacting to vivid evidence, ignoring base rates, or confusing likelihood with posterior confidence.

Core Features & Use Cases

Explicit belief-state updates: turns “what we think is true” into a structured update over priors/base rates, likelihood, and posterior confidence.
Evidence strength with competing hypotheses: compares how expected new evidence is under the hypothesis versus plausible alternatives to determine update direction.
Uncertainty honesty and calibration: supports qualitative or banded confidence updates and reports residual uncertainty plus what future evidence would change the posterior.

Quick Start

Use bayesian-reasoning to update your confidence about a hypothesis after new evidence, while explicitly stating the prior/base rate, the competing alternatives, and the likelihood comparison.

Frequently Asked Questions about bayesian-reasoning

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

FAQPage Schema

How do I update beliefs with new evidence without ignoring base rates?▼

Bayesian reasoning updates your posterior confidence by comparing how expected new evidence is under your hypothesis versus alternatives. It requires a prior base rate, explicit competing hypotheses, and a likelihood comparison to avoid confusing likelihood with posterior confidence.

What's the best way to avoid double counting correlated evidence in probabilistic forecasting?▼

To avoid correlated-evidence double counting in forecasting, explicitly separate likelihood from posterior confidence and evaluate evidence strength against plausible alternatives. This prevents artificial confidence inflation when updating beliefs with dependent evidence streams.

Can I use qualitative confidence bands for belief updates instead of exact probabilities?▼

Yes, qualitative or banded confidence updates are supported for belief revision. This maintains calibration by reporting residual uncertainty and identifying what future evidence would shift the posterior confidence without requiring exact probability calculations.

How does a likelihood ratio compare evidence strength across competing hypotheses?▼

The likelihood ratio compares how expected new evidence is under the primary hypothesis versus plausible alternatives. This comparison drives the update direction and magnitude, transforming prior base rates into calibrated posterior confidence.

When do I need to separate likelihood from posterior confidence in assumption revision?▼

Separate likelihood from posterior confidence in assumption revision whenever new evidence arrives and multiple alternatives exist. This prevents confusing how expected evidence is under a hypothesis with the actual probability of that hypothesis being true.