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Showing 1 to 20 of 243 for “"Monte Carlo Methods"”.
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Density based kinetic Monte Carlo methods
… Methode auf dieser Skala ist die Kinetic Monte Carlo (KMC) Methode. Basierend auf einer sorgfältigen Analyse wurden Möglichkeiten aufgezeigt, um die KMC Methode bzgl. Rechengeschwindigkeit zu optimieren. Besonderes Gewicht wurde dabei auf den technologisch wichtigen Fall höherer …
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Monte Carlo methods for lattice fields
Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
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Pricing European options using Monte Carlo methods
… of two options-pricing programs using Monte Carlo methods, one for a CPU and the other for a GPU. I also optimize them to reduce their running time. Finally I compare the performance of those two programs.
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A Review of Multilevel Monte Carlo Methods
The Monte Carlo method (MC) is a common numerical technique used to approximate an expectation that does not have an analytical solution. For certain problems, MC can be inefficient. Many techniques exist to improve the efficiency of MC methods. The Multilevel Monte Carlo (ML) technique developed …
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Multigroup cross section generation via Monte Carlo methods
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1997.
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Advanced Bayesian Monte Carlo Methods for Inference and Control
Monte Carlo methods are are an ubiquitous tool in modern statistics. Under the Bayesian paradigm, they are used for estimating otherwise intractable integrals arising when integrating a function $h$ with respect to a posterior distribution $\pi$. This thesis discusses several aspects of such Monte …
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Monte Carlo Methods for Reliability Analysis and Power Estimation
… Accurate and efficient power estimation methods are needed to allow such close interaction.
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Monte Carlo Methods for Motion Planning and Goal Inference
… nature of motion planning and prediction through Monte Carlo sampling techniques. We ensure (1) by shifting the focus from pure trajectory optimization to generating a variety of near-optimal paths, and achieve (2) by developing a prediction method capable of capturing the inherent multimodality …
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Monte Carlo methods for parallel processing of diffusion equations
A Monte Carlo algorithm for solving simple linear systems using a random walk is demonstrated and analyzed. The described algorithm solves for each element in the solution vector independently. Furthermore, it is demonstrated that this algorithm is easily parallelized. To reduce error, each …
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Constructive approaches to quasi-Monte Carlo methods for multiple integration
Recently, quasi-Monte Carlo methods have been successfully used for approximating multiple integrals in hundreds of dimensions in mathematical finance, and were significantly more efficient than Monte Carlo methods. To understand the apparent success of quasi-Monte Carlo methods for multiple …
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Novel Monte Carlo Methods for Large-Scale Linear Algebra Operations
… to scale to handle large data sets.</p> <p>Monte Carlo methods, which are based on statistical sampling, exhibit many attractive properties in dealing with large volume of datasets, including fast approximated results, memory efficiency, reduced data accesses, natural parallelism, and …
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Monte Carlo Methods: Application to Hydrogen Gas and Hard Spheres
The challenges in constructing an efficient CEIMC simulation center mostly around the noisy results generated from the QMC computations of the electronic energy. We introduce two complementary techniques, one for tolerating the noise and the other for reducing it. The penalty method modifies the …
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Monte Carlo methods: application to hydrogen gas and hard spheres
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties of quantum systems. The two major types of QMC we use are Variational Monte Carlo (VMC), which evaluates integrals arising from the variational principle, and Diffusion Monte Carlo (DMC), which …
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Backflow and pairing wave function for quantum Monte Carlo methods
Quantum Monte Carlo (QMC) methods are a class of stochastic techniques that can be used to compute the properties of electronic systems accurately from first principles. This thesis is mainly concerned with the development of trial wave functions for QMC. An extension of the backflow transformation …
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Structure in Machine Learning: Graphical Models and Monte Carlo Methods
… reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of many …
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Application of quantum Monte Carlo methods to molecular potential energy surfaces
<p>"Various computational methods have been used to generate potential energy surfaces, which can help us simulate and interpret how atoms or molecules behave during a chemical reaction. For accurate work, <i>ab initio</i> wavefunction methods have traditionally been used, which have some …
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Application of quantum Monte Carlo methods to excitonic and electronic systems
… with the application and development of quantum Monte Carlo (QMC) methods. We begin by proposing a technique to maximise the efficiency of the extrapolation of DMC results to zero time step, finding that a relative time step ratio of 1:4 is optimal. We discuss the post-processing of QMC data and …
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A Study in Hybrid Monte Carlo Methods in Computing Derivative Prices
Hybrid Monte Carlo (HMC) method is defined in this thesis as Monte Carlo method that utilizes conditional expectation so that the regular Monte Carlo method and other computational methods can be combined to price financial derivatives. This thesis introduces several hybrid Monte Carlo methods and …
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Quantum Monte Carlo Methods for First Principles Simulation of Liquid Water
… are very long, on the order of thousands of Monte Carlo cycles or picoseconds of Molecular Dynamics integration. Thus a great deal of computational effort is required to generate statistically independent, well-converged data. This problem affects all water simulations, but the implications …
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Quantum Monte Carlo methods for molecular systems: New developments and applications
… to: (i) expand the applicability of the quantum Monte Carlo (QMC) methods to larger molecular systems and check the reliability of traditional approaches, (ii) determine the impact of correlation energy on a variety of systems in which correlation is important, (iii) investigate new silicon …
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