bayesian-reasoning

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.