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Monte Carlo Methods and the Koksma-Hlawka Inequality
Mathematics, 2019 The solution of a wide class of applied problems can be represented as an integral over the trajectories of a random process. The process is usually modeled with the Monte Carlo method and the integral is estimated as the average value of a certain ...
Sergey Ermakov, Svetlana Leora
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Bayesian statistics and Monte Carlo methods
Journal of Geodetic Science, 2018 The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability.
Koch K. R.
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Multilevel Monte Carlo methods [PDF]
Acta Numerica, 2013 Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs.
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A comparative study between Classical Numerical Methods and Monte Carlo Methods [PDF]
المجلة العراقية للعلوم الاحصائية, 2008 Recently, there is a great interest in the methods of Monte Carlo used for the treatment of different technical and scientific issues. This research deals with using the Monte Carlo methods in numerical integration by making a general comparison between
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GENERALIZED SENSITIVITY ANALYSIS CAPABILITY WITH THE DIFFERENTIAL OPERATOR METHOD IN RMC CODE [PDF]
EPJ Web of Conferences, 2021 Sensitivity analysis is an important way for us to know how the input parameters will affect the output of a system. Therefore, recently, there is an increased interest in developing sensitivity analysis methods in continuous-energy Monte Carlo Code due ...
Shi Guanlin +3 more
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Randomization, Bootstrap and Monte Carlo Methods in Biology
, 1997 Preface to the Second Edition Preface to the First Edition Randomization The Idea of a Randomization Test Examples of Randomization Tests Aspects of Randomization Testing Raised by the Examples Sampling the Randomization Distribution or Systematic ...
B. Manly
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An LPC pole processing method for enhancing the identification of dominant spectral features
Electronics Letters, 2021 This paper proposes a new time‐resolved spectral analysis method based on a modification to the linear predictive coding (LPC) method for enhancing the identification of the dominant frequencies of a signal.
Jin Xu, Mark Davis, Ruairí de Fréin
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Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy [PDF]
, 2017 Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science.
Sanjib Sharma
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Convergence of Monte Carlo methods for neutron noise [PDF]
EPJ Web of ConferencesThe neutron noise δφ describes the small variations of the neutron flux around the stationary state φ0, and is typically due to vibrations or oscillations of the core components, induced by fluid-structure interactions and other generally unwanted ...
Fauvel Axel +3 more
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Automatic differentiable Monte Carlo: Theory and application
Physical Review Research, 2023 Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored.
Shi-Xin Zhang, Zhou-Quan Wan, Hong Yao
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