https://www.chenk.top/en/tags/probability-theory/Recent content in Probability Theory on Chen Kai BlogHugoenThu, 22 Jan 2026 09:00:00 +0000https://www.chenk.top/en/ml-math-derivations/03-probability-theory-and-statistical-inference/Thu, 22 Jan 2026 09:00:00 +0000https://www.chenk.top/en/ml-math-derivations/03-probability-theory-and-statistical-inference/<p><figure class="article-figure"> <img src="https://blog-pic-ck.oss-cn-beijing.aliyuncs.com/posts/en/ml-math-derivations/03-Probability-Theory-and-Statistical-Inference/illustration_1.png" alt="ML Math Derivations (3): Probability Theory and Statistical Inference — Chapter overview" loading="lazy" decoding="async" class="content-image"> </figure> </p> <hr> <h2 id="what-you-will-learn" class="heading-anchor">What You Will Learn<a href="#what-you-will-learn" class="heading-link" aria-label="Permalink to this section" title="Copy link to this section">#</a> </h2><p>In 1912, Ronald Fisher introduced <strong>maximum likelihood estimation</strong> in a short paper that quietly redefined statistics. His insight was almost embarrassingly simple: <em>if a parameter setting makes the observed data extremely likely, it is probably correct</em>. Almost every modern learning algorithm — from logistic regression to large language models — descends from this idea.</p>