From fd5014eb15521753f0371cd0e8a81dfaabf4c1c3 Mon Sep 17 00:00:00 2001
From: Split <yourstruly@danesabo.com>
Date: Fri, 13 Feb 2026 11:52:58 -0500
Subject: [PATCH] Digitize BSP class notes (4 files)

---
 .../continuous-mixed-random-variables.md      | 40 ++++++++++++++++
 .../joint-distributions.md                    | 37 +++++++++++++++
 .../monte-carlo-integration.md                | 47 +++++++++++++++++++
 .../variance-and-convergence.md               | 26 ++++++++++
 4 files changed, 150 insertions(+)
 create mode 100644 Fleeting Notes/Class/Bayesian Signal Processing/continuous-mixed-random-variables.md
 create mode 100644 Fleeting Notes/Class/Bayesian Signal Processing/joint-distributions.md
 create mode 100644 Fleeting Notes/Class/Bayesian Signal Processing/monte-carlo-integration.md
 create mode 100644 Fleeting Notes/Class/Bayesian Signal Processing/variance-and-convergence.md

diff --git a/Fleeting Notes/Class/Bayesian Signal Processing/continuous-mixed-random-variables.md b/Fleeting Notes/Class/Bayesian Signal Processing/continuous-mixed-random-variables.md
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+# Continuous and Mixed Random Variables
+
+**Source:** probabilitycourse.com → Hossein Pishro-Nik  
+**Date:** Monday January 26th
+
+---
+
+## What is a continuous random variable?
+
+> A random variable $X$ is **continuous** if its cumulative distribution function $F_X(x)$ is continuous for $x \in \mathbb{R}$.
+
+---
+
+## Probability Density Functions
+
+Continuous variables lend themselves to **Probability Density Functions** (PDFs). A PDF is defined as:
+
+$$f_X(x) = \frac{dF_X(x)}{dx}$$
+
+---
+
+## Example: Uniform Distribution
+
+**Consider** a uniform distribution of $x$ between $[a,b]$:
+
+**CDF:**
+$$F_X = \begin{cases} 0, & x < a \\ \frac{x-a}{b-a}, & a \leq x \leq b \\ 1, & x > b \end{cases}$$
+
+**PDF:**
+$$f_X = \begin{cases} 0, & x < a \\ \frac{1}{b-a}, & a \leq x \leq b \\ 0, & x > b \end{cases}$$
+
+*Note: $F_X$ is a continuous S-curve from 0 to 1 between $a$ and $b$; $f_X$ is a rectangular function with height $\frac{1}{b-a}$ between $a$ and $b$.*
+
+---
+
+## Probability from PDF
+
+Now that we've got some machinery, we can define:
+
+$$P(x \in [a,b]) = \int_a^b f_X(x) \, dx$$
diff --git a/Fleeting Notes/Class/Bayesian Signal Processing/joint-distributions.md b/Fleeting Notes/Class/Bayesian Signal Processing/joint-distributions.md
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+# Joint Distributions
+
+**Source:** probabilitycourse.com
+
+Joint distributions are **multivariate probability distributions**.
+
+---
+
+## Conditional Probability
+
+$$P(A|B) = \frac{P(A \cap B)}{P(B)}, \text{ when } P(B) > 0$$
+
+### Example: Fair Die
+
+If this is a fair die, what's the PMF of the outcomes given the event $A = \{x < 5\}$?
+
+$$P(A) = \frac{4}{6}$$
+
+$$P_{X|A}(1) = \frac{P(X = 1 \cap x < 5)}{P(x < 5)} = \frac{\frac{1}{6}}{\frac{4}{6}} = \frac{1}{4}$$
+
+$$P_{X|A}(2) = P_{X|A}(3) = P_{X|A}(4) = P_{X|A}(1) = \frac{1}{4}$$
+
+$$P_{X|A}(5) = \frac{P(x = 5 \cap x < 5)}{P(x < 5)} = \frac{0}{\frac{4}{6}} = 0$$
+
+$$P_{X|A}(6) = 0$$
+
+---
+
+## Two Random Variables
+
+When working with two random variables:
+
+$$P_{X|Y}(x_i | y_j) = P(X = x_i | Y = y_j)$$
+
+$$= \frac{P(X = x_i \cap Y = y_j)}{P_Y(y_j)}$$
+
+$$= \frac{P_{XY}(x_i, y_j)}{P_Y(y_j)}$$
diff --git a/Fleeting Notes/Class/Bayesian Signal Processing/monte-carlo-integration.md b/Fleeting Notes/Class/Bayesian Signal Processing/monte-carlo-integration.md
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+# Monte Carlo Integration
+
+$$I = \int_a^b f(x) \, dx$$
+
+---
+
+## Riemann Integration
+
+Pick $x_i = hi + a$
+
+$$I = \sum_{i=1}^{n} f(x_i) \frac{b-a}{n}$$
+
+This isn't the only way to integrate.
+
+---
+
+## Monte Carlo Integration
+
+Sample $X \sim U[a,b]$
+
+$$\hat{I} = \frac{b-a}{n} \sum_{i=1}^{n} f(x_i)$$
+
+> *This is technically a random variable.*
+
+### Expectation of the Estimator
+
+$$E\{f(x)\} = \int_a^b f(x) \cdot U[a,b] \, dx = \int_a^b f(x) \frac{1}{b-a} \, dx = \frac{1}{b-a} \int_a^b f(x) \, dx$$
+
+$$E\{\hat{I}\} = \frac{b-a}{n} \sum_{i=1}^{n} E\{f(x)\}$$
+
+$$= \frac{b-a}{n} \sum_{i=1}^{n} \left( \frac{1}{b-a} \int_a^b f(x) \, dx \right)$$
+
+$$= \frac{b-a}{n} \cdot \frac{n}{b-a} \int_a^b f(x) \, dx$$
+
+$$E\{\hat{I}\} = \int_a^b f(x) \, dx$$
+
+**Key insight:** The Monte Carlo estimator is unbiased — its expected value equals the true integral.
+
+---
+
+## Multi-Dimensional Case
+
+$$I = \iiint f(\vec{x}) \, d\vec{x}$$
+
+$$\hat{I} = \frac{V}{n} \sum_{i=1}^{n} f(\vec{x}_i)$$
+
+where $V$ is the volume of the integration region.
diff --git a/Fleeting Notes/Class/Bayesian Signal Processing/variance-and-convergence.md b/Fleeting Notes/Class/Bayesian Signal Processing/variance-and-convergence.md
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+++ b/Fleeting Notes/Class/Bayesian Signal Processing/variance-and-convergence.md	
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+# Variance and Convergence
+
+## Definition of Variance
+
+$$\text{Var}(X) = E\{(x - \mu)^2\}$$
+
+---
+
+## Variance of the Monte Carlo Estimator
+
+$$\text{Var}(\hat{I}) = \frac{(b-a)^2}{n} \text{Var}(f(x))$$
+
+### Convergence Rate
+
+Standard deviation scales as:
+$$\sigma \sim \frac{1}{\sqrt{n}}$$
+
+This is good for a small number of dimensions.
+
+**However:** With higher dimensionality, variance gets exponentially worse (curse of dimensionality).
+
+---
+
+## Multi-Dimensional Case
+
+Combine with Monte Carlo Integration techniques (importance sampling, stratified sampling, etc.) to manage variance in high dimensions.