Variance Reduction Techniques - Statistical Simulation · Introduction

Introduction

What this guide covers

Rigorous theoretical foundations, demonstrations, algorithms and fully solved exercises for techniques that reduce Monte Carlo error without increasing the number of samples.

Overview

Topics covered

Introduction
Variance Reduction Techniques
General Theoretical Framework: The Variance Problem
VRT Taxonomy
Antithetic Variables
1.1 Mathematical Foundation

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

\hat{I}_N^{(C)} = \frac{1}{N}\sum_{i=1}^{N} f(X_i), \qquad X_i \stackrel{\text{i.i.d.}}{\sim} \mu

View LaTeX source

\hat{I}_N^{(C)} = \frac{1}{N}\sum_{i=1}^{N} f(X_i), \qquad X_i \stackrel{\text{i.i.d.}}{\sim} \mu