# Monte Carlo method

## Top PDF Monte Carlo method:

### Uncertainty analysis of ANN based spectral analysis using Monte Carlo method

Abstract. Uncertainty analysis of an Artificial Neural Network (ANN) based method for spectral analysis of asynchronously sampled signals is performed. Main uncertainty components contributions, jitter and quantization noise, are considered in order to obtain the signal amplitude and phase uncertainties using Monte Carlo method. The analysis performed identifies also uncertainties main contributions depending on parameters configurations. The analysis is per- formed simultaneously with the proposed method and two others: Discrete Fourier Transform (DFT) and Multiharmonic Sine Fitting Method (MSFM), in order to compare them in terms of uncertainty. Results show the proposed method has the same uncertainty as DFT for amplitude values and around dou- ble uncertainty in phase values.
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12 Lee mas

### Size dependence study of the ordering temperature in the Fast Monte Carlo method

Monte Carlo (MC) simulations are widely used as a theoretical approach for studying magnetic properties in several types of systems (Landau and Binder 2000). By using this approach, thermal averages of some proper- ties can be obtained when a suitable Hamiltonian is known. Since dipolar energy is a long-range interaction, it becomes decisive to define the behavior of a magnetic system and its dynamical evolution (Mejı´a-Lo´pez et al. 2010). For large enough systems, at least in uniform or quasi-uniform states, the dipolar energy gives a contri- bution proportional to the volume of the system. Since the volume of a system increases with the number of spins; the higher this number, the stronger the effect of the dipolar term on the system configuration. Whenever MC simulations are carried out on systems whose size lies within the experimentally studied range of lenghts (*10–500 nm), every spin can interact, at the same time, at least with other 10 8 –10 9 spins approximately. This number of interactions must be counted for each spin in each MC step, doing the calculation very time consuming. Since the computation time of the dipolar term increases as the square of the number of particles, the study of most of the real systems becomes not reachable with the current computational facilities within reasonable amounts of time.
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12 Lee mas

### Implementation of a Monte Carlo method to model photon conversion for solar cells.

To study the potential gain in photocurrent in a solar cell when converter layers are implemented on it, a sensitivity analysis of the influence of the different set of parameters des[r]

### Numerical results of Monte Carlo code in lidar returns considering polarization of light and different phase functions

Monte Carlo method (MCM) is a useful tool to simulate and understand random process in the nature. Many of this process are difficult to solve by means of analytic expressions, due we do not know many of its variables involved in the process. The core of MCM is to generate random variables, which represent physical variables, through of its probability distribution function (PDF). Every random physical variable has a law of probability to obtain a certain value, within a specific interval. Specially, in the data obtained by lidar returns, we got information of the type of scatterer from the radiation backscatter to receiver. This portion of backscatter radiation is a little part of the function that describes the total scattering in radians. This function is called “phase function” and has a particular way depending on size, refractive index, shape, etc. of the scatterer. In this work we present the numerical results to consider different phase functions in the simulation of lidar returns, through of MCM.
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### Research Of Technological Properties Of The Styrene-Butadiene Rubber By The Method Of Mathematical Modeling

On the basis of production experiments and analysis of literary sources, the influence of molecular weight on the resulting copolymers on one of the important technological parameters of synthetic rubber - Mooney viscosity was established. In this paper a dependence is obtained that relates the Mooney viscosity and the molecular weight characteristics of the resulting product – the number-average and weight-average molecular weight. Molecular weight characteristics were determined by the modeling of the copolymerization process on the basis of Monte Carlo method. In this paper, we obtained a dependence linking the molecular weight characteristics of the resulting styrene-butadiene copolymer and the Mooney viscosity of the rubber specimen. For the derivation, the logarithmic regression dependence was used, which was applied to the results of modeling the process of styrene-butadiene copolymerization carried out in accordance with the conduct of this process in an industrial environment. The deviation of the data obtained as a result of applying the derived dependence to the simulation results from the production was no more than 8%.
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12 Lee mas

### Clase 6 (4/9): El mtodo de Monte Carlo

1.1. Limit Theorems . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2. Monte Carlo method . . . . . . . . . . . . . . . . . . . . . . . 2 2. Confidence interval for Monte Carlo method 3 2.1. Example 1: Computation of a probability . . . . . . . . . . . 3 2.2. Example: Computation of π . . . . . . . . . . . . . . . . . . . 4 2.3. Application: computing integrals by the rejection method . . 5 2.4. Example: Computation of a double integral . . . . . . . . . . 5 3. Example 2: Computation of an integral by the sample mean

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### El método de Monte Carlo y los desarrollos asintóticos

El año 1949 se considera como el del nacimiento del método Monte Carlo, debido a la aparición del artículo “The Monte Carlo method”. Su creación suele vincularse a los nombres de los matemáticos norteamericanos J. Von Neumann y S. Ulam; sin embargo, la base teórica del método se conocía desde mucho antes. Se puede afirmar que algunos problemas de la estadística se resolvían empleando las muestras aleatorias, es decir, aplicando, de hecho, el método Monte Carlo.

### Optimal decision policy for real options under general markovian dynamics

In this thesis, we propose a novel simulation approach to solve for optimal decision policies in real option problems under general Markovian dynamics. Our algorithm is implemented for the classical commodity mine of Brennan & Schwartz [The Journal of Business, 58(2), 135-157, 1985] under a wide variety of underlying dynamics such as stochastic variance, jumps, and regime-dependent parameters. In our numerical analysis, the method provides an accurate approximation of the critical prices when the underlying price follows a standard geometric Brownian motion. Moreover, the optimal policies produced by our algorithm are more profitable than those delivered by the widely-used Least-Squares Monte Carlo Method when the commodity follows more general dynamics. Finally, the algorithm allows to easily obtain the critical prices under regime-dependent dynamics, which are not accessible for backward methods based on forward simulation schemes.
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78 Lee mas

### Anisotropía de superficie en nanopartículas de magnetita: simulación de Monte Carlo

tetrahedral and octahedral sites respectively), the different coordination number and the superexchange integrals distribution function, are taken into account in order to get a simulation of the system as realistic as possible. Simulations were performed in the framework of the Monte Carlo method using a classical Heisenberg Hamiltonian including first nearest magnetic neighbors interactions and using the Metropolis dynamics. Energy, magnetization, specific heat and magnetic susceptibility, as function of temperature are computed.

### Método de Monte Carlo para el cálculo de integrales n-dimensionales

Los m´ etodos de Monte Carlo (MC) son un conjunto de m´ etodos num´ ericos estoc´ asticos que utilizan variables aleatorias bajo un modelo probabil´ıstico. Se considera el nacimiento del m´ etodo de Monte Carlo en el a˜ no 1949, en un art´ıculo divulgado por los matem´ aticos norte- americanos Nicholas Metropolis y S. Ulam, con el t´ıtulo “THE MONTE CARLO METHOD”. El nombre “Monte Carlo” se debe a una poblaci´ on del principado de M´ onaco, c´ elebre por su casa de juego [1], por la analog´ıa a los juegos de azar que generan n´ umeros aleatorios, como es el caso de la ruleta.
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### Numerical methods for option pricing.

Again it can be seen that MatLab simulation using matrices, without loops is much faster than version with loops, in fact it is approximately 100 times faster. The difference is even greater when MCdt.m saves the paths. Advantage of MCvectorized.m against all the other MCs so far is that it stores paths in matrix, and one can now calculate American options, or any of the Exotic options. When the code produces paths but doesn’t save them, not all kind of options can be valued (e.g. American using Longstaff Schwarz method). Vectorized version though has one flaw, it requires a lot of memory so first three cases are near the maximum use of memory (on author’s Lenovo laptop).Third case is basically the same as 2 nd case in table 2.2.1, but due different implementation it is slower, although not that drastic. Further, 4 th example is useless in the sense of saving paths of the options, and it is presented in order to compare it MC1s results. In fact, both third and fourth examples converge to the right value at the same pace, and it doesn’t matter if many or just one step is used. This is implied by equation 2.2.1.
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47 Lee mas

### Cálculo de la incertidumbre por simulación de Monte Carlo en la determinación de aflatoxina B1 en maní de exportación por HPLC-FD. Aplicación a la evaluación de la conformidad. Parte II

En la figura 3 se presentó el esquema general para el cálculo de la incertidumbre por simulación numérica de Monte Carlo. Se generan los valores de las variables aleatorias que influyen en el mensurando y, se introducen en la función f del modelo matemático (ecuación 1) y se calcula el valor de y. El proceso se repite 5000 veces y se calcula la incertidumbre como desviación estándar. Cálculo usando Maple

11 Lee mas

### Dispersión compton en mamografía: Estudio por simulación Monte Carlo

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41 Lee mas

### Bayesian statistics in genetics

(1997) Mapping linked quantitative trait loci using Bayesian analysis and Markov chain Monte Carlo algorithms. (1996) The use of multiple markers in a Bayesian method for mapping quantit[r]

### View of Standard uncertainty calculation by Monte Carlo technique for topography and hole-filed displacement measurement means ESPI | Nova Scientia

Para evaluar la incertidumbre estándar de las mediciones de entrada se necesita considerar la manera en cómo la medición está hecha. Si las mediciones de entrada se repiten varias veces bajo las mismas condiciones, entonces la evaluación de la incertidumbre será de tipo A; por otro lado si las mediciones de entrada se realizan una sola vez a través de otros modelos matemáticos o son importados de otras fuentes la evaluación será de tipo B [v] . En muchos casos las evaluaciones de la incertidumbre de tipo B puede realizarse mediante la técnica de simulación de Monte Carlo con la función de densidad de probabilidad apropiada. En este trabajo se utilizó esta técnica para obtener las incertidumbres de las mediciones correspondientes a la topografía y al campo de desplazamiento en la dirección x donde todas las mediciones de entrada son del tipo B.
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### UNIVESIDAD AUTONOMA METROPOLITANA IZTAPALAPA INFORME DE PROYECTO TERMINAL I

simulación numérica de Monte Carlo, para obtener las propiedades termodinámicas tales como presiÓn,energia configuracional entre. otras, y de estructura como la función[r]

57 Lee mas

### Núm. 45 (2009)

Table 2 lists the final results of the uranium enrichment of the 17 ensemble fuel elements and 14 additional uranium pellets determined by gamma spectrometry. Absolute method shows bigger dispersion and deviation for the expected reference value (0.72%) than the “simple method”. This agreed quiet well with the dispersion (9.10%) and deviation for the expected values (-4.90%), obtained for Absolute method during an international exercise for uranium enrichment measurements celebrated in 2000 [18]. On the other hand, the precision and accuracy of the «simethod” results are excellent. The last one confirms the small dependence of sample characteristics like density and geometric shape of this method.
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