惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
Help Net Security
Help Net Security
The Hacker News
The Hacker News
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
G
GRAHAM CLULEY
Simon Willison's Weblog
Simon Willison's Weblog
V
Vulnerabilities – Threatpost
A
Arctic Wolf
T
Tor Project blog
Jina AI
Jina AI
C
CXSECURITY Database RSS Feed - CXSecurity.com
C
Cyber Attacks, Cyber Crime and Cyber Security
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Spread Privacy
Spread Privacy
Security Latest
Security Latest
Latest news
Latest news
Last Week in AI
Last Week in AI
博客园 - 司徒正美
P
Privacy International News Feed
T
Tenable Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
腾讯CDC
博客园 - 聂微东
Scott Helme
Scott Helme
爱范儿
爱范儿
P
Proofpoint News Feed
C
CERT Recently Published Vulnerability Notes
I
Intezer
博客园 - Franky
酷 壳 – CoolShell
酷 壳 – CoolShell
L
LINUX DO - 热门话题
有赞技术团队
有赞技术团队
S
Secure Thoughts
WordPress大学
WordPress大学
The Cloudflare Blog
AWS News Blog
AWS News Blog
B
Blog RSS Feed
Cyberwarzone
Cyberwarzone
S
Security Affairs
Recent Commits to openclaw:main
Recent Commits to openclaw:main
The Register - Security
The Register - Security
L
LINUX DO - 最新话题
博客园_首页
C
Cybersecurity and Infrastructure Security Agency CISA
博客园 - 【当耐特】
T
Troy Hunt's Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
S
Securelist
S
Schneier on Security
Application and Cybersecurity Blog
Application and Cybersecurity Blog

John D. Cook

DNA sequence alignment and Delannoy numbers Silver Rectangles and the Ways of Kings Derivative equals inverse Who you gonna believe: Grok or the docs? Brace expansion tree When will the decimals in a/b repeat? Height of harmonic numbers Writing down harmonic numbers Hart’s theorem Incircles and Excircles of Pythagorean triangles Regular expressions that work “everywhere” Consecutive Pythagorean triangle sides The Star Trek lemma Lobachevsky’s integral formula Queens on a prime order board All pieces on a 6 by 5 board Formalizing a ring theorem with Lean 4 and Claude Partial fraction decomposition Three examples suffice Testing pentagonal numbers Quaternion Rotations, Claude, and Lean Writing Prolog with ChatGPT RSA munitions T-shirt Solving a chess puzzle with Claude and Prolog Formally proving a calculation with Claude and Lean Pulling on a thread Aitken acceleration before Aitken The Laplace limit A crank formula for π From Kepler to Bessel Mr. Bessel’s eponymous functions
Distinguishing variables from parameters
John · 2026-07-01 · via John D. Cook

Imagine the following dialog.

Professorf is a function of a real variable x that takes a real parameter k.

Student: What’s a parameter?

Professor: It’s a constant that can vary.

Student: Then if it can vary, isn’t it a variable?

Professor: Sorta, but no not really.

This conversation plays out over and over, and unfortunately it often ends as it does above, with the student confused. Here’s how I believe the conversation should continue.

Professor: You’re absolutely right that f is a function of two variables, x and k. But usually k is fixed in the context of a specific application and x is not. A different application might have a different, but also fixed, value of k. So it is helpful to think of f(xk), a function of x with a parameter k, rather than f(xk), a function of two variables. The former carries more information, giving a hint as to how the numbers are used.

Is there really a difference between a parameter and a variable? In a reductionistic sense, no. But in a practical sense, yes, absolutely.

It might sound pedantic to distinguish a variable from a parameter, and it is, in the best sense of the word. Pedant literally means teacher. Usually pedantic carries a negative connotation, such as making a distinction without a difference. But here the pedant would be making a helpful distinction.

For example, we might write a probability density function as f(x; μ, σ). The function gives the probability density at a point x. The density depends on parameters μ and σ, and these parameters change between applications, but for a given application they have fixed values.

You find the probability of a random variable taking on values in an interval [ab] by integrating f over that interval. When I say that, you know that I mean you’d integrate with respect to x, because f is a function of x. It is also, in an abstract sense, a function of μ and σ, but it’s typically not useful to think of it that way.

Hypergeometric functions have two sets of parameters, and so you may see two semicolons, such as f(xabc). This denotes a function of the variable x, with upper parameters a and b, and a lower parameter c. In some abstract sense this is a function of four variables, but it acts very differently with respect to x than with respect to ab, and c. There’s also a difference between a and b on the one hand an c on the other, one worth paying attention to, though it is less of a difference than between x and the parameters collectively.

Sometimes you’ll see a vertical bar rather than a semicolon to separate variables from parameters. This works out even better for probability densities because then f(x | μ, σ) suggests the probability density of x given μ and σ since the vertical bar is also used for conditional probability. You might also see f(xa, b; c) for hypergeometric functions, with the vertical bar separating variables from parameters and the semicolon separating two kinds of parameters.

When I first saw a semicolon separating variables from parameters, no explanation was given, and I figured I could mentally replace the semicolon with a comma. Then later I realized that the semicolon was an act of kindness by the author giving the reader additional information.