From 4cf8cfd0793fedbff90dbcc73a069cba13c802f4 Mon Sep 17 00:00:00 2001 From: Caleb Burke Date: Sat, 30 May 2026 00:14:44 -0700 Subject: [PATCH] small chapter 3 prog --- .../ch3/notes.ipynb | 50 ++++++++++++++++++- 1 file changed, 49 insertions(+), 1 deletion(-) diff --git a/study/001_introduction-to-probability-statistics-and-random-processes/ch3/notes.ipynb b/study/001_introduction-to-probability-statistics-and-random-processes/ch3/notes.ipynb index 58c31e2..6146982 100644 --- a/study/001_introduction-to-probability-statistics-and-random-processes/ch3/notes.ipynb +++ b/study/001_introduction-to-probability-statistics-and-random-processes/ch3/notes.ipynb @@ -31,7 +31,7 @@ "id": "9f0046c2", "metadata": {}, "source": [ - "## Random Variables (3.1.1 - 3.1.2)" + "## Random Variables (3.1.1 - 3.1.3)" ] }, { @@ -99,6 +99,54 @@ "cell_type": "markdown", "id": "57323d97", "metadata": {}, + "source": [ + "## Independent Random Variables (3.1.4)" + ] + }, + { + "cell_type": "markdown", + "id": "c6261f7d", + "metadata": {}, + "source": [ + "***Definition.*** Consider two discrete random variables $X$ and $Y$. We say that $X$ and $Y$ are independent if:\n", + "\n", + "$$P(X=x, Y=y) = P(X=x)P(Y=y)$$\n", + "\n", + "In general, if two random variables are independent, then you can write\n", + "\n", + "$$P(X \\in A, Y \\in B) = P(X \\in A)(Y \\in B)$$" + ] + }, + { + "cell_type": "markdown", + "id": "7b0362c1", + "metadata": {}, + "source": [ + "***Definition.*** Consider $n$ discrete random variables $X_1, X_2, X_3, \\dots, X_n$. We say that $X_1, X_2, X_3, \\dots, X_n$ are independent if:\n", + "\n", + "$$P(X_1 = x_1, X_2 = x_2, X_3 = x_3, \\dots, X_n = x_n) = P(X_1 = x_1)P(X_2 = x_2) \\dots P(X_n = x_n)$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "e50c7c48", + "metadata": {}, + "source": [ + "## Special Distributions (3.1.5)" + ] + }, + { + "cell_type": "markdown", + "id": "853e9acb", + "metadata": {}, + "source": [ + "***Definition.*** A random variable $X$ is said to be a *Bernoulli* random variable with parameter *p*, shown as $X \\sim \\text{Bernoulli}(p),$ if its PMF is given by" + ] + }, + { + "cell_type": "markdown", + "id": "9e1caee1", + "metadata": {}, "source": [] } ],