Statistical computing: Random variable generation and Monte Carlo methods

Introduction

What this guide covers

A rigorous mathematical and algorithmic approach to stochastic simulation, from pseudorandom number synthesis to Markov Chain sampling methods.

Overview

Topics covered

• Introduction
• Generation of Random Variables and Monte Carlo Methods
• Introduction to Pseudorandom Number Generation (PRNG)
• 1.1 Linear Congruential Generators (LCG)
• Lattice Structure and Marsaglia's Theorem
• 1.2 Modern Generators

Related concepts

Main connections

• Variance & standard deviation
• Markov chain Monte Carlo (MCMC)
• Calculus & differentiation
• Monte Carlo simulation
• Time series & ARIMA
• Linear algebra & matrices

Compatible notation

Mathematical sample in LaTeX

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