Why the Monte Carlo method is so important today

  title={Why the Monte Carlo method is so important today},
  author={Dirk P. Kroese and Tim J. Brereton and Thomas Taimre and Zdravko I. Botev},
  journal={Wiley Interdisciplinary Reviews: Computational Statistics},
Since the beginning of electronic computing, people have been interested in carrying out random experiments on a computer. Such Monte Carlo techniques are now an essential ingredient in many quantitative investigations. Why is the Monte Carlo method (MCM) so important today? This article explores the reasons why the MCM has evolved from a ‘last resort’ solution to a leading methodology that permeates much of contemporary science, finance, and engineering. WIREs Comput Stat 2014, 6:386–392. doi… 

Stitching Monte Carlo samples

Monte Carlo (MC) simulations are extensively used for various purposes in modern high-energy physics (HEP) experiments. Precision measurements of established Standard Model processes or searches for

The principle and application of Monte Carlo simulation in public health, finance, and physics

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Approximation of the Monte Carlo Sampling Method for Reliability Analysis of Structures

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Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.

This study develops a novel GPU-based Metropolis MC (gMMC) with a novel path-by-path simulation scheme and demonstrates its effectiveness in an example problem of scattered X-ray photon calculation in CT.

Comparison of uncertainty analysis of the Montecarlo and Latin Hypercube algorithms in a camera calibration model

The results show the advantages of the Latin Hypercube method over the Monte Carlo method, taking into account the number of executions of the model, maintaining a 95 percent confidence level and reducing the execution time considerably.

Bayesian Probabilistic Numerical Integration with Tree-Based Models

A new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which this paper calls BART-Int, which is easy to tune and well-suited for discontinuous functions.

Correlation effects in parallel tempering and the role of the swapping frequency.

  • I. Tavernelli
  • Physics
    Physical chemistry chemical physics : PCCP
  • 2020
This work shows that high frequency swaps can induce a systematic bias on the sampled REMD equilibrium distributions, and should serve as a monitor for using too frequent swapping attempts in parallel tempering simulations of generic Hamiltonians, including the ones used in atomistic simulations.



Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)

The authoritative resource for understanding the power behind Monte Carlo Methods and a new co-author has been added to enliven the writing style and to provide modern day expertise on new topics.

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research.

Monte Carlo strategies in scientific computing

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.


T he year was 1945. Two earth-shaking events took place: the successful test at Alamogordo and the building of the first electronic computer. Their combined impact was to modify qualitatively the

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  • R. Rubinstein
  • Physics
    Wiley series in probability and mathematical statistics
  • 1981
From the Publisher: Provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This

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We show that multigrid ideas can be used to reduce the computational complexity of estimating an expected value arising from a stochastic differential equation using Monte Carlo path simulations. In

Fast Sequential Monte Carlo Methods for Counting and Optimization

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A new approach to Monte Carlo simulations is presented, giving a highly efficient method of simulation for large systems near criticality. The algorithm violates dynamic universality at second-order

Equation of state calculations by fast computing machines

A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method