Log in

Relevant bibliographies by topics / Approche Monte-Carlo / Journal articles

To see the other types of publications on this topic, follow the link: Approche Monte-Carlo.

Journal articles on the topic 'Approche Monte-Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Approche Monte-Carlo.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Tanguy, D., and B. Legrand. "Ségrégation et précipitation intergranulaires : une approche par simulation Monte-Carlo." Le Journal de Physique IV 09, PR4 (1999): Pr4–39—Pr4–44. http://dx.doi.org/10.1051/jp4:1999405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Autin, Claude, Jacques Fearnley, and Ronald Rioux. "Effets des erreurs dans les coefficients structuraux d’un modèle intersectoriel « rectangulaire ». Une approche de type Monte-Carlo." L'Actualité économique 51, no. 1 (2009): 86–95. http://dx.doi.org/10.7202/800607ar.

Full text

Abstract:

The most simple rectangular input-output models use two rectangular matrices: R a market coefficient matrix, A* a production coefficient matrix. A given exogenous demand Xo determines the sectorial activity levels X* = [I — RA*]-1Xo. We assume that A* is random with expectation A. We study the distribution of the "error" X* — X with X = [I — RA]-1Xo. (1) For the statistically independent elements of A*, we analytically prove that X < EX*. (2) In the more realistic case of statistically dependent elements of A*. (a) One submatrix of A* with T non zero elements is chosen. The probabilistic mo

APA, Harvard, Vancouver, ISO, and other styles

3

Jacquemod, G., Y. Charlon, Z. Wei, Y. Leduc, and P. Lorenzini. "Application de la technologie FDSOI pour la conception de nouvelles topologies de circuits analogiques et mixtes." J3eA 18 (2019): 1021. http://dx.doi.org/10.1051/j3ea/20191021.

Full text

Abstract:

Pour poursuivre la loi de Moore avec des noeuds technologiques de 22 nm et en deçà, les transistors MOS bulk ont été remplacés par des transistors FinFET ou UTBB-FDSOI. Ces derniers disposent d’une grille arrière permettant de réaliser de nouvelles topologies de circuits analogiques et mixtes, offrant des performances jamais atteintes et réduisant certaines limitations, comme par exemple celles liées à la réduction de la longueur du canal. Partant de la caractéristique de la tension de seuil d’un transistor UTBB-FDSOI en fonction de la polarisation de la grille arrière, nous proposons aux élèv

APA, Harvard, Vancouver, ISO, and other styles

4

Gusak, Andriy, and Tetiana Zaporozhets. "Martin’s Kinetic Mean-Field Model Revisited—Frequency Noise Approach versus Monte Carlo." METALLOFIZIKA I NOVEISHIE TEKHNOLOGII 40, no. 11 (2018): 1415–35. http://dx.doi.org/10.15407/mfint.40.11.1415.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Tichý, Tomáš. "Examination of selected improvement approaches to Monte Carlo simulation in option pricing." Politická ekonomie 56, no. 6 (2008): 772–94. http://dx.doi.org/10.18267/j.polek.663.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhu, Jiang, Nafis Ahmad, Hideichi Nakamoto, and Nobuto Matsuhira. "1A1-E32 Map Building and Localization of Home Robot using Feature Matching and Monte Carlo Approach." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _1A1—E32_1—_1A1—E32_4. http://dx.doi.org/10.1299/jsmermd.2006._1a1-e32_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Taylor, Mark, Vince Kwasnica, Denis Reilly, and Somasundaram Ravindran. "Game theory modelling of retail marketing discount strategies." Marketing Intelligence & Planning 37, no. 5 (2019): 555–66. http://dx.doi.org/10.1108/mip-11-2018-0489.

Full text

Abstract:

Purpose The purpose of this paper is to use the game theory combined with Monte Carlo simulation modelling to support the analysis of different retail marketing strategies, in particular, using payoff matrices for modelling the likely outcomes from different retail marketing strategies. Design/methodology/approach Theoretical research was utilised to develop a practical approach for applying game theory to retail marketing strategies via payoff matrices combined with Monte Carlo simulation modelling. Findings Game theory combined with Monte Carlo simulation modelling can provide a formal appro

APA, Harvard, Vancouver, ISO, and other styles

8

Coulibaly, N., and B. Wade Brorsen. "Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results." Econometric Reviews 18, no. 2 (1999): 195–209. http://dx.doi.org/10.1080/07474939908800439.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Gogu, Christian, Anirban Chaudhuri, and Christian Bes. "How Adaptively Constructed Reduced Order Models Can Benefit Sampling-Based Methodsfor Reliability Analyses." International Journal of Reliability, Quality and Safety Engineering 23, no. 05 (2016): 1650019. http://dx.doi.org/10.1142/s0218539316500194.

Full text

Abstract:

Many sampling-based approaches are currently available for calculating the reliability of a design. The most efficient methods can achieve reductions in the computational cost by one to several orders of magnitude compared to the basic Monte Carlo method. This paper is specifically targeted at sampling-based approaches for reliability analysis, in which the samples represent calls to expensive finite element models. The aim of this paper is to illustrate how these methods can further benefit from reduced order modeling to achieve drastic additional computational cost reductions, in cases where

APA, Harvard, Vancouver, ISO, and other styles

10

Giles, Michael B. "Multilevel Monte Carlo methods." Acta Numerica 24 (April 27, 2015): 259–328. http://dx.doi.org/10.1017/s096249291500001x.

Full text

Abstract:

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. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.In this article, we review the ideas beh

APA, Harvard, Vancouver, ISO, and other styles

11

Dai, Hongsheng, Murray Pollock, and Gareth Roberts. "Monte Carlo fusion." Journal of Applied Probability 56, no. 01 (2019): 174–91. http://dx.doi.org/10.1017/jpr.2019.12.

Full text

Abstract:

AbstractIn this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.), but to date the framework and methodology proposed in this paper (Monte Carlo fusion) is the first general approach which avoids any form of approximation error in obtaining the unified inference. In this paper we focus on the key theoretical u

APA, Harvard, Vancouver, ISO, and other styles

12

SWENDSEN, ROBERT H., BRIAN DIGGS, JIAN-SHENG WANG, SHING-TE LI, CHRISTOPHER GENOVESE, and JOSEPH B. KADANE. "TRANSITION MATRIX MONTE CARLO." International Journal of Modern Physics C 10, no. 08 (1999): 1563–69. http://dx.doi.org/10.1142/s0129183199001340.

Full text

Abstract:

Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations. In this paper, we describe a complementary approach to extracting information from Monte Carlo simulations that uses the matrix of transition probabilities. Combining the Transition Matrix with an N-fold way simulation technique produces an extremely flexible and efficient approach to rather general Monte Carlo simulations.

APA, Harvard, Vancouver, ISO, and other styles

13

Alexandrov, Vassil, and Oscar A. Esquivel-Flores. "Towards Monte Carlo preconditioning approach and hybrid Monte Carlo algorithms for Matrix Computations." Computers & Mathematics with Applications 70, no. 11 (2015): 2709–18. http://dx.doi.org/10.1016/j.camwa.2015.08.035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Yuan, Yu, Hendrix Demers, Samantha Rudinsky, and Raynald Gauvin. "Secondary Fluorescence Correction for Characteristic and Bremsstrahlung X-Rays Using Monte Carlo X-ray Depth Distributions Applied to Bulk and Multilayer Materials." Microscopy and Microanalysis 25, no. 1 (2019): 92–104. http://dx.doi.org/10.1017/s1431927618016215.

Full text

Abstract:

AbstractSecondary fluorescence effects are important sources of characteristic X-ray emissions, especially for materials with complicated geometries. Currently, three approaches are used to calculate fluorescence X-ray intensities. One is using Monte Carlo simulations, which are accurate but have drawbacks such as long computation times. The second one is to use analytical models, which are computationally efficient, but limited to specific geometries. The last approach is a hybrid model, which combines Monte Carlo simulations and analytical calculations. In this article, a program is develope

APA, Harvard, Vancouver, ISO, and other styles

15

Hong-Xin, Huang, Lian Shi-Xun, and Cao Ze-Xing. "Surplus Function Quantum Monte Carlo Approach." Acta Physico-Chimica Sinica 15, no. 07 (1999): 599–605. http://dx.doi.org/10.3866/pku.whxb19990705.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Austin, Brian M., Dmitry Yu Zubarev, and William A. Lester. "Quantum Monte Carlo and Related Approaches." Chemical Reviews 112, no. 1 (2011): 263–88. http://dx.doi.org/10.1021/cr2001564.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Gutman, Ivan, Slavko Radenković, Ante Graovac, and Dejan Plavšić. "Monte Carlo approach to Estrada index." Chemical Physics Letters 446, no. 1-3 (2007): 233–36. http://dx.doi.org/10.1016/j.cplett.2007.08.053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Krauth, Werner, Hermann Nicolai, and Matthias Staudacher. "Monte Carlo approach to M-theory." Physics Letters B 431, no. 1-2 (1998): 31–41. http://dx.doi.org/10.1016/s0370-2693(98)00557-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Szep, J., J. Cserti, and J. Kertesz. "Monte Carlo approach to dendritic growth." Journal of Physics A: Mathematical and General 18, no. 8 (1985): L413—L418. http://dx.doi.org/10.1088/0305-4470/18/8/002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Tessarotto, Massimo, Roscoe B. White, and Lin‐Jin Zheng. "Probabilistic approach to Monte Carlo operators." Physics of Plasmas 1, no. 8 (1994): 2591–602. http://dx.doi.org/10.1063/1.870586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Tessarotto, Massimo, Roscoe B. White, and Lin‐Jin Zheng. "Monte Carlo approach to collisional transport." Physics of Plasmas 1, no. 8 (1994): 2603–13. http://dx.doi.org/10.1063/1.870587.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Pesonen, Janne, and Eero Hyvönen. "Interval approach challenges Monte Carlo simulation." Reliable Computing 2, no. 2 (1996): 155–60. http://dx.doi.org/10.1007/bf02425918.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Acquah, Henry de-Graft. "A Comparison of Bootstrap and Monte Carlo Approaches to Testing for Symmetry in the Granger and Lee Error Correction Model." Information Management and Business Review 5, no. 5 (2013): 240–44. http://dx.doi.org/10.22610/imbr.v5i5.1048.

Full text

Abstract:

In this paper, I investigate the power of the Granger and Lee model of asymmetry via bootstrap and Monte Carlo techniques. The simulation results indicate that sample size, level of asymmetry and the amount of noise in the data generating process are important determinants of the power of the test for asymmetry based on bootstrap and Monte Carlo techniques. Additionally, the simulation results suggest that both bootstrap and Monte Carlo methods are successful in rejecting the false null hypothesis of symmetric adjustment in large samples with small error size and strong levels of asymmetry. In

APA, Harvard, Vancouver, ISO, and other styles

24

Zhou, Changxi, Jing Chen, Gerard T. Schuster, and Brackin A. Smith. "A quasi‐Monte Carlo approach to efficient 3-D migration: Field data test." GEOPHYSICS 64, no. 5 (1999): 1562–72. http://dx.doi.org/10.1190/1.1444660.

Full text

Abstract:

The quasi‐Monte Carlo migration algorithm is applied to a 3-D seismic data set from West Texas. The field data were finely sampled at approximately 220-ft (67-m) intervals in the in‐line direction but were sampled coarsely at approximately 1320-ft (402-m) intervals in the cross‐line direction. The traces at the quasi‐Monte Carlo points were obtained by an interpolation of the regularly sampled traces. The subsampled traces at the quasi‐Monte Carlo points were migrated, and the resulting images were compared to those obtained by migrating both regular and uniform grids of traces. Results show t

APA, Harvard, Vancouver, ISO, and other styles

25

Ramadan, Helmi, Prana Ugiana Gio, and Elly Rosmaini. "Monte Carlo Simulation Approach to Determine the Optimal Solution of Probabilistic Supply Cost." Journal of Research in Mathematics Trends and Technology 2, no. 1 (2020): 1–6. http://dx.doi.org/10.32734/jormtt.v2i1.3752.

Full text

Abstract:

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probabilitydistribution from data variable collected based on historical data. The used model is probabilistic Economic Order Quantity Model (EOQ). This model then assumed use Monte Carlo simulation, so that obtained the total of optimal supply cost in the future. Based on data processing, the result of probabilistic EOQ is $486128,19. After simulation using Monte Carlo simulation where the demand data follows normal distribution and it is obtained

APA, Harvard, Vancouver, ISO, and other styles

26

Grana, Dario, Leonardo Azevedo, and Mingliang Liu. "A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data." GEOPHYSICS 85, no. 4 (2020): WA41—WA52. http://dx.doi.org/10.1190/geo2019-0405.1.

Full text

Abstract:

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies and petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity for their ability to obtain accurate solutions for geophysical inverse problems in which the physical models are partially unknown. Solutions of classification and inversion problems are generally not unique, and uncertainty quantification studies are required to quantify the uncertainty in the model predictions and determine the precision of the results. Probab

APA, Harvard, Vancouver, ISO, and other styles

27

Makarova, K. V., A. G. Makarov, M. A. Padalko, V. S. Strongin, and K. V. Nefedev. "Multispin Monte Carlo Method." Dal'nevostochnyi Matematicheskii Zhurnal 20, no. 2 (2020): 212–20. http://dx.doi.org/10.47910/femj202020.

Full text

Abstract:

The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means. The cluster multispin approach extends the computational capabilities of the Metropolis algorithm and allows one to find configurations of the ground and low-energy states.

APA, Harvard, Vancouver, ISO, and other styles

28

Deelstra, Griselda, Grégory Rayée, Steven Vanduffel, and Jing Yao. "USING MODEL-INDEPENDENT LOWER BOUNDS TO IMPROVE PRICING OF ASIAN STYLE OPTIONS IN LÉVY MARKETS." ASTIN Bulletin 44, no. 2 (2014): 237–76. http://dx.doi.org/10.1017/asb.2014.6.

Full text

Abstract:

AbstractAlbrecheret al. (Albrecher, H., Mayer Ph., Schoutens, W. (2008) General lower bounds for arithmetic Asian option prices.Applied Mathematical Finance,15, 123–149) have proposed model-independent lower bounds for arithmetic Asian options. In this paper we provide an alternative and more elementary derivation of their results. We use the bounds as control variates to develop a simple Monte Carlo method for pricing contracts with Asian-style features. The conditioning idea that is inherent in our approach also inspires us to propose a new semi-analytic pricing approach. We compare both app

APA, Harvard, Vancouver, ISO, and other styles

29

Jakumeit, J., U. Ravaioli, and K. Hess. "New Approach to Hot Electron Effects in Si-MOSFETs Based on an Evolutionary Algorithm Using a Monte Carlo Like Mutation Operator." VLSI Design 6, no. 1-4 (1998): 307–11. http://dx.doi.org/10.1155/1998/81023.

Full text

Abstract:

We introduce a new approach to hot electron effects in Si-MOSFETs, based on a mixture of evolutionary optimization algorithms and Monte Carlo technique. The Evolutionary Algorithm searchs for electron distributions which fit a given goal, for example a measured substrate current and in this way can calculate backwards electron distributions from measurement results. The search of the Evolutionary Algorithm is directed toward physically correct distributions by help of a Monte Carlo like mutation operator. Results for bulk-Si demonstrate the correctness of the physical model in the Monte Carlo

APA, Harvard, Vancouver, ISO, and other styles

30

Sauqi, Ahmad. "ANALYSIS OF BEEF INVENTORY PLANNING WITH APPROACH MONTE CARLO METHOD IN CV. PUTRA SURYA." MBA - Journal of Management and Business Aplication 3, no. 2 (2020): 316–24. http://dx.doi.org/10.31967/mba.v3i2.358.

Full text

Abstract:

Research with the title Analysis of Planning Beef Inventory with Monte Carlo Method Approach in CV. Putra Surya was held for 3 months, from September to November 2019. The data used in this research is secondary data obtained from the owner of CV. Putra Surya. Based on the results of the analysis that hasbeen done with Monte Carlo Simulation, CV. Putra Surya can plan beef supply for 2020 of 35,388 kg of beef with an average demand in one week of 707.76 kg. For expected requests or expected values of 704.96. The average demand that has been processed by the Monte Carlo Simulation method of 707.

APA, Harvard, Vancouver, ISO, and other styles

31

Crevillén-García, D., and H. Power. "Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media." Royal Society Open Science 4, no. 8 (2017): 170203. http://dx.doi.org/10.1098/rsos.170203.

Full text

Abstract:

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 the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equat

APA, Harvard, Vancouver, ISO, and other styles

32

CAZENAVE, TRISTAN. "MONTE-CARLO EXPRESSION DISCOVERY." International Journal on Artificial Intelligence Tools 22, no. 01 (2013): 1250035. http://dx.doi.org/10.1142/s0218213012500352.

Full text

Abstract:

Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on m

APA, Harvard, Vancouver, ISO, and other styles

33

Lara, D. Peña, and J. A. Plascak. "The Critical Behavior of the General Spin Blume–Capel Model." International Journal of Modern Physics B 12, no. 20 (1998): 2045–61. http://dx.doi.org/10.1142/s0217979298001198.

Full text

Abstract:

The general spin-S Blume–Capel model is studied within two different approaches: the pair approximation for the free energy, and Monte Carlo simulations. The global phase diagram in the temperature-anisotropy plane is obtained for general values of S in the pair approximation and the results are qualitatively the same as those of the usual mean field theory. Special interest is given in the low temperature region of the phase diagram where a number of first-order lines emerge from a multiphase point at the ground state. Monte Carlo simulations for S=1, 3/2, and 2 on simple cubic lattices also

APA, Harvard, Vancouver, ISO, and other styles

34

Khoshkholgh, Sarouyeh, Andrea Zunino, and Klaus Mosegaard. "Informed proposal Monte Carlo." Geophysical Journal International 226, no. 2 (2021): 1239–48. http://dx.doi.org/10.1093/gji/ggab173.

Full text

Abstract:

SUMMARY Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on the fly, and much improvement has been made in this way. However, as a consequence of the No-Free-Lunch Theorem, the only way we can ensure a significantly better performance of search and sampling algorithms is to build in as much external information about the problem as possible. In the special case of Markov Chain Monte Carlo (MCMC) sampling we review how this is done through the choice of proposal distribution, and we

APA, Harvard, Vancouver, ISO, and other styles

35

Aloisio, Roberto. "COMPUTATIONAL SCHEMES FOR THE PROPAGATION OF ULTRA HIGH ENERGY COSMIC RAYS." Acta Polytechnica 53, A (2013): 703–6. http://dx.doi.org/10.14311/ap.2013.53.0703.

Full text

Abstract:

We discuss the problem of ultra high energy particles propagation in astrophysical backgrounds. We present two different computational schemes based on kinetic and Monte Carlo approaches. The kinetic approach is an analytical computation scheme based on the hypothesis of continuos energy losses while the Monte Carlo scheme takes into account also the stochastic nature of particle interactions. These schemes, which give quite reliable results, enable the computation of fluxes keeping track of the different primary and secondary components, providing a fast and useful workbench for studying Ultr

APA, Harvard, Vancouver, ISO, and other styles

36

Fathi Vajargah, Behrouz, and Farshid Mehrdoust. "Partitioning Inverse Monte Carlo Iterative Algorithm for Finding the Three Smallest Eigenpairs of Generalized Eigenvalue Problem." Advances in Numerical Analysis 2011 (April 11, 2011): 1–9. http://dx.doi.org/10.1155/2011/826376.

Full text

Abstract:

A new Monte Carlo approach for evaluating the generalized eigenpair of real symmetric matrices will be proposed. Algorithm for the three smallest eigenpairs based on the partitioning inverse Monte Carlo iterative (IMCI) method will be considered.

APA, Harvard, Vancouver, ISO, and other styles

37

Everitt, Richard G., Richard Culliford, Felipe Medina-Aguayo, and Daniel J. Wilson. "Sequential Monte Carlo with transformations." Statistics and Computing 30, no. 3 (2019): 663–76. http://dx.doi.org/10.1007/s11222-019-09903-y.

Full text

Abstract:

AbstractThis paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. W

APA, Harvard, Vancouver, ISO, and other styles

38

Bastin, Fabian, Cinzia Cirillo, and Stephane Hess. "Evaluation of Optimization Methods for Estimating Mixed Logit Models." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (2005): 35–43. http://dx.doi.org/10.1177/0361198105192100105.

Full text

Abstract:

The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi–Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data

APA, Harvard, Vancouver, ISO, and other styles

39

Veness, J., K. S. Ng, M. Hutter, W. Uther, and D. Silver. "A Monte-Carlo AIXI Approximation." Journal of Artificial Intelligence Research 40 (January 24, 2011): 95–142. http://dx.doi.org/10.1613/jair.3125.

Full text

Abstract:

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific

APA, Harvard, Vancouver, ISO, and other styles

40

Gattke, C., and A. Schumann. "Comparison of different approaches to quantify the reliability of hydrological simulations." Advances in Geosciences 11 (May 16, 2007): 15–20. http://dx.doi.org/10.5194/adgeo-11-15-2007.

Full text

Abstract:

Abstract. The focus of this study was to compare different uncertainty estimation approaches to evaluate their ability to predict the total amount of uncertainty in hydrological model predictions. Three different approaches have been compared. Two of them were based on Monte-Carlo sampling and the third approach was based on fitting a probability model to the error series of an optimized simulation. These approaches have been applied to a lumped and a semi-distributed model variant, to investigate the effects of changes in the model structure on the uncertainty assessment. The probability mode

APA, Harvard, Vancouver, ISO, and other styles

41

DEDENKO, L. G., A. A. MIRONOVICH, and I. M. ZHELEZNYKH. "HYBRID SCHEME OF SIMULATION OF ELECTRON-PHOTON AND ELECTRON-HADRON CASCADES IN DENSE MEDIUM AT ULTRA-HIGH ENERGIES." International Journal of Modern Physics A 21, supp01 (2006): 45–49. http://dx.doi.org/10.1142/s0217751x06033349.

Full text

Abstract:

The multilevel scheme to simulate electron-photon and electron-hadron cascades has been suggested. The Monte Carlo approach should be used to take into account fluctuations in a development of an individual cascade. Then transport equations should be exploited. The low energy particles should be treated again by the Monte Carlo approach.

APA, Harvard, Vancouver, ISO, and other styles

42

Guyon, Julien, and Pierre Henry-Labordère. "Uncertain Volatility Model: A Monte-Carlo Approach." Journal of Computational Finance 14, no. 3 (2011): 37–71. http://dx.doi.org/10.21314/jcf.2011.233.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Hong-Xin, Huang, and Zeng Xian-Biao. "Quantum Monte Carlo Approach Process Excited State." Acta Physico-Chimica Sinica 16, no. 08 (2000): 681–88. http://dx.doi.org/10.3866/pku.whxb20000803.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Dimov, Ivan, and Rayna Georgieva. "Adaptive Monte Carlo Approach for Sensitivity Analysis." Procedia - Social and Behavioral Sciences 2, no. 6 (2010): 7644–45. http://dx.doi.org/10.1016/j.sbspro.2010.05.158.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Albeanu, Grigore. "A Monte Carlo approach for controlled search." Mathematics and Computers in Simulation 43, no. 2 (1997): 223–28. http://dx.doi.org/10.1016/s0378-4754(96)00069-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Berizzi, Alberto, Cristian Bovo, Maurizio Delfanti, Marco Merlo, and Marco Savino Pasquadibisceglie. "A Monte Carlo Approach for TTC Evaluation." IEEE Transactions on Power Systems 22, no. 2 (2007): 735–43. http://dx.doi.org/10.1109/tpwrs.2007.895163.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Pulfer, James Douglas, and Clement Waine. "An Efficient Monte Carlo Approach to Optimization." Journal of Chemical Information and Computer Sciences 38, no. 5 (1998): 791–97. http://dx.doi.org/10.1021/ci970103+.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Campioni, Luca, Ruben Scardovelli, and Paolo Vestrucci. "Biased Monte Carlo optimization: the basic approach." Reliability Engineering & System Safety 87, no. 3 (2005): 387–94. http://dx.doi.org/10.1016/j.ress.2004.06.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Shapiro, A. "Monte Carlo sampling approach to stochastic programming." ESAIM: Proceedings 13 (December 2003): 65–73. http://dx.doi.org/10.1051/proc:2003003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Neirotti, J. P., David L. Freeman, and J. D. Doll. "Approach to ergodicity in Monte Carlo simulations." Physical Review E 62, no. 5 (2000): 7445–61. http://dx.doi.org/10.1103/physreve.62.7445.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!