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Self-learning Monte Carlo method: Continuous-time algorithm [PDF]
The self-learning Monte Carlo (SLMC) method speeds up the Monte Carlo simulation by designing and training an effective model to propose efficient global updates.
Y. Nagai+4 more
semanticscholar +3 more sources
Balancing the maintenance strategies to making decisions using Monte Carlo method [PDF]
This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings.
Khamiss Cheikh+3 more
doaj +2 more sources
A NEW MONTE CARLO METHOD FOR TIME-DEPENDENT NEUTRINO RADIATION TRANSPORT [PDF]
Monte Carlo approaches to radiation transport have several attractive properties such as simplicity of implementation, high accuracy, and good parallel scaling.
E. Abdikamalov+6 more
semanticscholar +5 more sources
GENERALIZED SENSITIVITY ANALYSIS CAPABILITY WITH THE DIFFERENTIAL OPERATOR METHOD IN RMC CODE [PDF]
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
doaj +1 more source
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. We review here the basic principles and the most common Monte Carlo algorithms, among which rejection sampling ...
Mrinal K. Sen, Paul L. Stoffa
+7 more sources
IMPLEMENTATION OF MONTE CARLO MOMENT MATCHING METHOD FOR PRICING LOOKBACK FLOATING STRIKE OPTION
Monte Carlo method was a numerical method that was popular in finance. This method had disadvantages at convergences, so the moment matching was used to improve the efficiency from Monte Carlo method.
Komang Nonik Afsari Dewi+2 more
doaj +1 more source
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
doaj +1 more source
A HYBRID MONTE-CARLO-DETERMINISTIC METHOD FOR AP1000 EX-CORE DETECTOR RESPONSE SIMULATION [PDF]
The ex-core detector-response calculation is a typical deep-penetration problem, which is challenging for the Monte Carlo method. The response of the ex-core detector is an important parameter for the safe operation of the nuclear power plants. Meanwhile,
Zheng Qi+6 more
doaj +1 more source
Quasi Monte Carlo for Periodic Review in Inventory Systems [PDF]
Periodic Review as a method is widely used especially in inventory system. In this paper Quasi Monte Carlo is used for simulating Periodic Review. The problem: How to implement Quasi Monte Carlo simulation in Periodic Review for inventory system of MSMEs
Sugiharti Endang+4 more
doaj +1 more source
Antithetic Magnetic and Shadow Hamiltonian Monte Carlo
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
doaj +1 more source