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<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="zh"><id>https://www.hugchange.life</id><title>学习知识小站</title><updated>2026-06-15T03:24:04.480362+00:00</updated><author><name>mE(metaEscape)</name><email>metaescape at foxmail dot com</email></author><link href="https://www.hugchange.life" rel="alternate"/><link href="https://www.hugchange.life/atom.xml" rel="self"/><generator uri="https://lkiesow.github.io/python-feedgen" version="1.0.0">python-feedgen</generator><subtitle>https://www.hugchange.life</subtitle><entry><id>/posts/202605_SAT</id><title>从求值到搜索：SAT 问题为什么难？</title><updated>2026-05-30T11:14:00+08:00</updated><content/><link href="https://www.hugchange.life//posts/202605_SAT.html"/><published>2026-05-21T23:01:00+08:00</published></entry><entry><id>/posts/202605_p_x-y</id><title>P(X|Y) 简介</title><updated>2026-05-25T12:11:00+08:00</updated><content>关于条件概率是什么的一些整理，包括黑箱属性和结构的加入</content><link href="https://www.hugchange.life//posts/202605_p_x-y.html"/><category term="概率"/><published>2026-05-17T11:45:00+08:00</published></entry><entry><id>/posts/2023_mathlogicpy/index</id><title>数理逻辑及其 python 实现</title><updated>2026-05-15T10:52:00+08:00</updated><content/><link href="https://www.hugchange.life//posts/2023_mathlogicpy/index.html"/><category term="逻辑"/><published>2023-10-30T15:26:00+08:00</published></entry><entry><id>/posts/202603_poisson_process</id><title>泊松过程</title><updated>2026-05-14T21:43:00+08:00</updated><content>泊松过程是在什么假设下获得的玩具模型？有哪些概率属性，如泊松分布，指数分布，合并性质</content><link href="https://www.hugchange.life//posts/202603_poisson_process.html"/><category term="概率"/><published>2026-05-14T21:07:00+08:00</published></entry><entry><id>/posts/202605_list_sampling</id><title>一个简单随机抽样问题的细节</title><updated>2026-05-14T17:29:00+08:00</updated><content>抽样统计某个有限总体样本均值里有哪些关于信念，频率，概率的问题?</content><link href="https://www.hugchange.life//posts/202605_list_sampling.html"/><category term="概率"/><published>2026-05-10T22:08:00+08:00</published></entry><entry><id>/posts/202605_e_exp</id><title>自然常数以及指数函数</title><updated>2026-05-09T10:42:00+08:00</updated><content>指数 e 是如何从从商业博弈中出现的，并且是如何把基于自身的线性比例重复增长推广到矩阵变换，旋转和信念的</content><link href="https://www.hugchange.life//posts/202605_e_exp.html"/><category term="结构"/><published>2026-05-04T17:42:00+08:00</published></entry><entry><id>/posts/202605_markov_stable_absorb</id><title>马尔可夫链中稳态概率和吸收概率对比</title><updated>2026-05-09T01:13:00+08:00</updated><content>二者都是基于全概率公式列出线性方程组并求解,有很大相似性</content><link href="https://www.hugchange.life//posts/202605_markov_stable_absorb.html"/><category term="概率"/><published>2026-05-02T20:17:00+08:00</published></entry><entry><id>/posts/202403_prob_limits</id><title>概率和统计间的旋转门 -- 极限理论</title><updated>2026-05-06T19:28:00+08:00</updated><content>
尝试回答：随机变量 X 依概率收敛于 Y, 是否意味着 X 和 Y 的相关系数为 1, 是否意味着 X 和 Y 遵循的两个分布很接近，比如 KL 散度为 0? 中心极限定理是如何推导，高斯分布的形式是如何出现的？...
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