Probability & Statistical Inference · Probability Theory

Computational aspects of probability · Theory and simulation

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Computational treatment of chance

Overview

Topics covered

  • Introduction
  • Computational Aspects of Probability: from the analytical model to the algorithm
  • Floating point arithmetic and numerical stability
  • 1.1 Operations in Logarithmic Space (Log-Space)
  • 1.2 The Log-Sum-Exponential Trick in Code
  • Pseudo-random number generation (PRNG)

Related concepts

Main connections

  • Markov chain Monte Carlo (MCMC)
  • Bootstrap resampling
  • Variance & standard deviation
  • Monte Carlo simulation
  • Bayes' theorem & priors
  • Maximum likelihood estimation

Compatible notation

Mathematical sample in LaTeX

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

x=(−1)s×(1+∑i=152bi2−i)×2e−1023
View LaTeX sourcex = (-1)^s \times \left(1 + \sum_{i=1}^{52} b_i 2^{-i}\right) \times 2^{e - 1023}