数理统计学导论

Hacker News 上怎么说

• stanleydrew · 2009 年

  ……我经济学博士项目的第一年。不过最好还是从一本基础的概率论与数理统计教材开始，最好是数学推导比较多的那种。我用过 Hogg、McKean 和 Craig 写的《Introduction to Mathematical Statistics》，确实非常棒。

  …the first year of my PhD program in Economics. But it's probably worth starting with a basic probability and statistics book, preferably one that's pretty mathematical. I've used Hogg McKean and Craig's Introduction to Mathematical Statistics and it's really good.

• _eigenfoo · 2018 年

  如果你只是想找数理统计方面的书，我挺喜欢 Hogg、McKean 和 Craig 的《Introduction to Mathematical Statistics》（在我看来第 4 版比后面的版本好得多）。但如果你学概率/统计是为了做 ML 应用，大多数 ML 教材都会有一两章来复习相关内容。我挺喜欢……

  If you're looking for just mathematical statistics, I liked Hogg McKean and Craig's "Introduction to Mathematical Statistics" (4th edition was much better than the ones after it, imo). But if you're looking to learn prob/stats for applications to ML, most ML textbooks have a chapter or two reviewing the relevant stuff. I liked…

• luhbeg · 2022 年

  ……提醒自己某些分析证明是怎么推导的。线性代数：Strang 的《Intro to LA》很棒，从这本开始；Strang 的《LA and learning from data》，要是没看第一本会很难啃。统计学：Hogg 的《Introduction to Mathematical Statistics》；贝叶斯看 Gelman 等人的《BDA3》。ML：Bishop 的《PRML》和《Elements of Statistical Learning》。多做课后习题。用 Python 把算法手搓一遍。

  …remind myself how certain analysis proofs go. Linear Algebra Strang, Intro to LA is great. Start with this one Strang, LA and learning from data. Will be tough without the first book Stats Hogg, Introduction to Mathematical Statistics Gelman et al, BDA3 for Bayes ML Bishop PRML and Elements of statistical learning. Do the exercises. Build the algos in Python.

• Lesabotsy · 2020 年

  ……我主要需要概率论、数理统计、微积分和线性代数。目前我正在学预备微积分（Stewart）。接下来的规划是：概率论：Blitzstein & Hwang 的《Introduction to Probability》。统计学：Hogg 等人的《Introduction to Mathematical Statistics》。微积分：James Stewart 等人的《Calculus, Early Transcendentals》。线性代数：Gilbert Strang 的《Introduction to Linear Algebra》。在这些科目上你有啥推荐吗？学这些够用吗？……

  …I mostly need Probability, Statistics, Calculus and Linear Algebra. For now I'm studying precalc (Stewart). Next steps are: Probability: Introduction to Probability - Blitzstein & Hwang. Statistics: Introduction to Mathematical Statistics - Hogg & al. Calculus: Calculus, Early Transcendentals - James Stewart & al. Linear Algebra: Introduction to Linear Algebra - Gilbert Strang. What are your picks in those subject? Are these enough?…

• nikkev · 2016 年

  我以前上一门统计推断课时，我们结合参考了《Statistical Inference》（Casella 和 Berger 著）、《Introduction to Mathematical Statistics》（Hogg 和 Craig 著）以及《Probability and Statistics》（Degroot 和 Schervish 著）。如果你仍然想了解费歇尔信息量（Fisher Information）和克拉美-罗下界（Cramer-Rao Lower Bound），可以参考第 514 - 521 页……

  When I was taking a class in Statistical Inference, we used a combination of Statistical Inference (Casella and Berger), Introduction to Mathematical Statistics (Hogg and Craig) and Probability and Statistics (Degroot and Schervish). If you're still interested in learning about Fisher Information and the Cramer-Rao Lower Bound, you can refer to pages 514 - 521…

• golol · 2024 年

  我觉得你搜索数学资料的方法可能不太对。如果我是你，我会去搜书名或讲义名带“Introduction to probability theory”或者“Introduction to mathematical statistics”的。然后快速翻翻看能不能找到针对你这个问题的分析。如果找不到，也希望能从中摸清正确的关键词，再去搜针对具体……的更高级讲义或书。

  I feel like you must be a using a wrong approach to searching for mathematics. In your case I would search for a script or book with title "Introduction to probabilify theory" or "Introduction to mathematical statistics". Then you skim through it to see if you can find an analysis of your problem. If not, you can hopefully find out what the right keywords are to then find a more advanced script or book for the specific…

正在加载完整评论……