Numerical methods: simulation and resampling methods · Introduction

Introduction

What this guide covers

A deep analytical exposition on the fundamentals of probabilistic computing, from the foundations of measure theory to advanced MCMC schemes and non-parametric resampling.

Overview

Topics covered

Introduction
Numerical Methods: Stochastic Simulation and Resampling Algorithms
Fundamentals of Sampling and Stochastic Simulation
1.1 Probability Spaces, Random Variables and Simulation
1.2 Convergence Theorems
1.3 Classical Monte Carlo Simulation: Fundamental Identity

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

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

\bar{X}_n = \frac{1}{n} \sum_{i=1}^n X_i \xrightarrow{c.s.} \mathbb{E}[X_1] \quad \text{cuando } n \to \infty

View LaTeX source

\bar{X}_n = \frac{1}{n} \sum_{i=1}^n X_i \xrightarrow{c.s.} \mathbb{E}[X_1] \quad \text{cuando } n \to \infty