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think-probabilistic

Compute posterior beliefs using Bayesian updating and Dempster-Shafer reasoning.

1|Updated Apr 13, 2026
Search Tags:#evidence#probability#uncertainty#decision-making#bayesian#posterior#evidential
One-click install
npx skills add https://github.com/danielsimonjr/deepthinking-plugin --skill think-probabilistic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: think-probabilistic
Source: https://github.com/danielsimonjr/deepthinking-plugin/tree/main/skills/think-probabilistic
Command: npx skills add https://github.com/danielsimonjr/deepthinking-plugin --skill think-probabilistic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Handles uncertainty in reasoning by offering structured probabilistic methods (Bayesian updating and Evidential reasoning) to update beliefs and fuse multi-source evidence.

Core Features & Use Cases

  • Bayesian inference for updating prior beliefs with new data and computing posteriors across sequential evidence.
  • Evidential reasoning (Dempster-Shafer) to combine evidence from multiple sources and express ignorance and conflict.
  • Use cases include risk assessment, decision support under uncertainty, hypothesis evaluation, and multi-source evidence synthesis.

Quick Start

Provide a hypothesis and a stream of evidence, and I will compute a posterior probability using Bayesian updating or Dempster–Shafer reasoning.

Frequently Asked Questions about think-probabilistic

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

FAQPage Schema
How do I update prior probabilities with sequential evidence using Bayesian inference?▼

Bayesian inference updates prior beliefs by computing posterior probabilities across sequential evidence streams. You provide a hypothesis and new data, and the system calculates updated posteriors to support decision-making under uncertainty.

What is the best way to handle ignorance and conflict when combining multi-source evidence?▼

Evidential reasoning, specifically Dempster-Shafer theory, represents ignorance and combines multi-source evidence by calculating mass functions. This approach explicitly handles conflict between sources better than standard probabilistic methods.

Can I use Dempster-Shafer reasoning for multi-source risk assessment under uncertainty?▼

Yes, Dempster-Shafer reasoning supports risk assessment by fusing multi-source evidence into structured outputs. It computes belief masses and posterior beliefs, enabling decision support even when source reliability is uncertain.

When should I use Evidential reasoning instead of Bayesian updating for hypothesis evaluation?▼

Use Evidential reasoning when you need to represent ignorance or combine conflicting multi-source evidence. Use Bayesian updating when you have defined prior probabilities and sequential data to compute precise posterior beliefs.

How does probabilistic reasoning compute posterior beliefs for decision support?▼

Probabilistic reasoning computes posterior beliefs by applying Bayesian inference or Evidential reasoning to your hypotheses. It processes likelihoods, priors, and masses to produce structured outputs for decision support under uncertainty.

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