Results 1 to 10 of about 708,503 (346)
EPJ Web of Conferences, 2013 Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo methods are sampling algorithms that allow to compute these integrals numerically when they are not analytically tractable.
Bardenet Rémi
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Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media [PDF]
Royal Society Open Science, 2017 In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during ...
D. Crevillén-García, H. Power
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Antithetic Magnetic and Shadow Hamiltonian Monte Carlo
IEEE Access, 2021 Hamiltonian Monte Carlo is a Markov Chain Monte Carlo method that has been widely applied to numerous posterior inference problems within the machine learning literature. Markov Chain Monte Carlo estimators have higher variance than classical Monte Carlo
Wilson Tsakane Mongwe +2 more
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IET Generation, Transmission & Distribution, 2021 Integrated energy system (IES) is an effective solution for energy and environment problems. In view of the difficulty of traditional reliability assessment methods to reasonably and effectively assess the reliability of the IES, an energy supply ...
Zhenkun Li +3 more
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Clock Monte Carlo methods [PDF]
Physical Review E, 2019 We propose the clock Monte Carlo technique for sampling each successive chain step in constant time. It is built on a recently proposed factorized transition filter and its core features include its O(1) computational complexity and its generality.
Michel, Manon +2 more
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Quantum Monte Carlo Methods for Astrophysical Applications
Frontiers in Physics, 2020 In recent years, new astrophysical observations have provided a wealth of exciting input for nuclear physics. For example, the observations of two-solar-mass neutron stars put strong constraints on possible phase transitions to exotic phases in strongly ...
Ingo Tews
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MENENTUKAN HARGA OPSI DENGAN METODE MONTE CARLO BERSYARAT MENGGUNAKAN BARISAN KUASI ACAK FAURE
E-Jurnal Matematika, 2021 An option contract is a contract that gives the owner the right to sell or even to buy an asset at the predetermined price and period time. The conditional Monte Carlo is one of the several methods that is used to determine the option price which in the ...
PUTU WIDYA ASTUTI +2 more
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E-Jurnal Matematika, 2022 Estimating the value at risk (VaR) is an important aspect of investment. VaR is a standard method of measuring risk defined as the maximum loss over a certain period of time at a certain level of confidence.
PUTU SAVITRI DEVI +2 more
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Shell model Monte Carlo methods [PDF]
Physics Reports, 1997 We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; the resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation
Koonin, S. E., Dean, D. J., Langanke, K.
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Journal of Management Science and Engineering, 2022 The generalized likelihood ratio (GLR) method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances.
Yijie Peng +4 more
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