# Bayesian Epistemology

> 【EN】We can think of belief as an all-or-nothing affair. For example, I believe that I am alive, and I don’t believe that I am a historian of the Mongol Empire. However, often we want to make distinctions between how strongly we believe or disbelieve something. I strongly believe that I am alive, am fairly confident that I will stay alive until my next conference presentation, less confident that the presentation will go well, and strongly disbelieve that its topic will concern the rise and fall of the Mongol Empire. The idea that beliefs can come in different strengths is a central idea behind Bayesian epistemology. Such strengths are called degrees of belief , or credences . Bayesian epistemologists study norms governing degrees of beliefs, including how one’s degrees of belief ought to change in response to a varying body of evidence. Bayesian epistemology has a long history. … 【中】词条以教程方式展开：先给出贝叶斯认识论的两个核心规范，再介绍应用；随后讨论贝叶斯阵营内部的分歧——融贯到底要求什么、先验概率问题怎么解决；接着考察用荷兰书论证为这些规范奠基的尝试、其他替代基础，以及对条件化的各种反驳。

- ID: m13201
- Category: culture
- Domain: 认识论

## Definition

贝叶斯认识论把信念看成有强弱之分的：信念度（又称置信度）刻画你相信某事的强度，理性要求这些强度满足概率公理，并在新证据到来时按条件化的方式更新。 脚手架作用：- 量化信念强度：用概率而非信或不信表达把握程度，使分歧可被精确比较。 - 规范证据更新：按贝叶斯公式把新证据折算成信念度的升降，避免过度或不足。

## How it works

信念度一旦可用数字表示，概率演算就为它们提供了一致性约束；而贝叶斯定理给定了在证据上更新信念度的正确方式。学习因此不再是信念的翻转，而是整个信念分布被证据精确修订。

## Practice

1) 给待考察的各个假设指派先验信念度；2) 估计在各假设下出现该证据的似然；3) 用贝叶斯定理算出后验信念度；4) 检查整套信念度是否融贯；5) 随新证据反复条件化并观察收敛情况。

## Use

- 量化信念强度：用概率而非信或不信表达把握程度，使分歧可被精确比较。 - 规范证据更新：按贝叶斯公式把新证据折算成信念度的升降，避免过度或不足。

[Read the web page](https://thinkingmodels.site/en/entries/detail/m13201)