From c0f7dfd5f4bcd170bdc33222f729686eca9f7b41 Mon Sep 17 00:00:00 2001 From: Youwen Wu Date: Sun, 16 Feb 2025 13:10:03 -0800 Subject: [PATCH] fix: cmf -> pmf --- src/posts/2025-02-16-probability-distributions.md | 2 +- typst/2025-02-16-probability-distributions.typ | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/posts/2025-02-16-probability-distributions.md b/src/posts/2025-02-16-probability-distributions.md index 8cea2e2..ebbf22d 100644 --- a/src/posts/2025-02-16-probability-distributions.md +++ b/src/posts/2025-02-16-probability-distributions.md @@ -82,7 +82,7 @@ $$P(X \leq b) = \int_{- \infty}^{b}f(x)dx$$ for all $b \in {\mathbb{R}}$, then $f$ is the **probability density function** (hereafter abbreviated p.d.f. or PDF) of $X$. -We immediately see that the p.d.f. is analogous to the c.d.f. of the +We immediately see that the p.d.f. is analogous to the p.m.f. of the discrete case. The probability that $X \in ( - \infty,b\rbrack$ is equal to the area diff --git a/typst/2025-02-16-probability-distributions.typ b/typst/2025-02-16-probability-distributions.typ index 9a0dda7..3277161 100644 --- a/typst/2025-02-16-probability-distributions.typ +++ b/typst/2025-02-16-probability-distributions.typ @@ -92,7 +92,7 @@ Now as promised we introduce another major class of random variables. abbreviated p.d.f. or PDF) of $X$. ] -We immediately see that the p.d.f. is analogous to the c.d.f. of the discrete case. +We immediately see that the p.d.f. is analogous to the p.m.f. of the discrete case. The probability that $X in (-infinity, b]$ is equal to the area under the graph of $f$ from $-infinity$ to $b$.