科学与数学github.com编辑精选
面向黑客的贝叶斯方法（免费 Python 电子书）
Free Python ebook: Bayesian Methods for Hackers
2013 — 2020，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这是 CamDavidsonPilon 的免费开源电子书《Bayesian Methods for Hackers》的 GitHub 仓库，用 Python 代码和交互式 notebook 介绍贝叶斯方法与概率编程。它强调边运行代码、边调参数、边看图来学习，适合有编程基础的人动手实践。
为什么经久不衰：开源协作和可交互 notebook 让读者能直接改代码、修错误并参与改进，这种“可玩的教材”模式使它多年反复被 Hacker News 讨论。
HN 评论精选
批评该书数学严谨性不足，解释混乱且符号缺少说明。
我想唱个反调：这本书可能不是学习统计推断的好方式。它对贝叶斯方法和频率派方法的解释相当混乱；序言似乎暗示程序员只要能使用计算机，就不需要上严格的贝叶斯数学课，但正文通篇使用数学符号，而且据我所知常常没有解释。我至少注意到一处：伽马分布只通过图来描述，没有给出概率密度函数或推导方式。我认为这本书代表的话语半只脚已经踏进了“货物崇拜式统计”。
mariuskempe查看英文原文
I should like to give a contrarian comment about this, because it is on top of the front page and seems to be being received positively. This book is probably not a good way to learn about statistical inference. It has quite confused explanations of both Bayesian and frequentist approaches. The preface seems to imply that programmers, by virtue of being able to use computers, don't need to take a rigorous mathematical course in Bayesian methods. However the text actually uses mathematical notation throughout, and as far as I could tell it is often not explained. I noticed at least one case where a probability distribution (gamma) was described only through plots i.e. without specifying its pdf or how you could derive the pdf. I think the kind of discourse that this book exemplifies is halfway to cargo cult 'statistics'.
notebook 可交互是最大优点，开源也让读者容易修错和提交补丁。
我几周前读了这本书，很喜欢 IPython notebook 格式。读的时候能编辑每张图的代码、摆弄参数，真是一种享受。额外收获是，我学做漂亮图表学到的东西和学贝叶斯方法差不多。书里有不少笔误，但边读边改源码、读完后提交补丁的切换成本很低；要不是这么方便，我大概不会花时间去做。
onedognight查看英文原文
I read this book a few weeks ago. I loved the ipython notebook format. Being able to edit the code for each figure and play around with parameters while reading was a treat. As a bonus, I learned as much a about making nice figures as I did about Bayesian Methods. There were quite a few typos, but it wasn't much of a context switch to edit the source as I found them and then submit patches when I was done. I probably wouldn't have taken the time if it wasn't as convenient.
赞赏开放、多人协作的写书模式。
这本书的开发方式很不寻常：内容是开源的，意味着任何人都可以成为作者，作者们通过 GitHub 界面提交内容或修订。这真是件好事——这本书看起来很棒，但它的制作方式更胜一筹。我真希望基于 IPython 或其他工具的交互式 notebook，以及多作者在 GitHub 上协作，能变得越来越普遍。
recuter查看英文原文
"This book has an unusual development design. The content is open-sourced, meaning anyone can be an author. Authors submit content or revisions using the GitHub interface." What a wonderful thing -- this book looks fantastic, but the approach to making it really takes the cake. I really hope interactive notebooks (iPython based or otherwise) and multiple authors collaborating on Github will become widespread.
询问概率编程在工业界的应用现状，以及它与深度学习的关系。
我对深度学习很熟悉，因为它被高度炒作；但我对概率编程和贝叶斯方法没那么熟。所以想问个一般性问题：工业界有人在用概率编程吗？大家是不是已经弃用它、转向深度神经网络了？还是有人在尝试把两者混合起来？
zengid查看英文原文
I'm quite familiar with deep learning because it's so highly hyped up, but am less familiar with Probabilistic programming and Bayesian methods. So, I have a general question: is anyone using Probabilistic Programming in industry? Have people ditched it for DNNs? Are people taking hybrid approaches to try and mix the two?
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