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Showing 1 to 14 of 14 for “"Quasi-Monte Carlo"”.
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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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Modeling and quasi-Monte Carlo simulation of risk in credit portfolios
… recovery values, or position sizes, Monte Carlo methods are necessary to capture their underlying dynamic evolutions. Since the feasibility of the Monte Carlo methods is limited by their relatively slow convergence rate, methods to improve the efficiency of simulations for credit …
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Quasi-Monte Carlo and Picard Iteration Algorithms for the Nonlinear Hydrodynamics, Dynamics and Controls of Wave Energy Converters
… Impulse Theory. The proposed framework debuts Quasi-Monte Carlo (QMC) spatial integration in nonlinear wave-body interactions, in conjunction with a new extension of Modified Chebyshev Picard Iteration compatible with the fluid force impulses. A mesh-free geometric representation using signed …
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Construction of lattice rules for multiple integration based on a weighted discrepancy
… multidimensional integrals, one may use Monte Carlo methods in which the quadrature points are generated randomly or quasi-Monte Carlo methods, in which points are generated deterministically. One particular class of quasi-Monte Carlo methods for multivariate integration is represented by …
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KVA in Black Scholes Pricing
… tree method and a combination of the crude and quasi-Monte Carlo method.
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Pricing swaptions on amortising swaps
… a comparison with the prices generated using Monte Carlo methods. Two methods were used to accelerate the convergence rate of the Monte Carlo model, a variance reduction method, namely the control variates technique and a method of using deterministic low-discrepancy sequences (also called …
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Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
… sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a …
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Qualitative and quantitative convergence results for randomised integration methods
… integration methods based on either, randomised Quasi-Monte Carlo, or (adaptive) Markov chain Monte Carlo methods are studied. Depending on the underlying integration problem we show qualitative and quantitative results, which ensure the asymptotic correctness of an algorithm or provide explicit …
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Various Approximate Methods to Measure the Uniformity of Quasirandom Sequences
In many Monte Carlo applications, one can substitute the use of pseudorandom numbers with quasirandom numbers and achieve improved convergence. This is because quasirandom numbers are more uniform than pseudorandom numbers. The most common measure of that uniformity is the star discrepancy. In …
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Computational Bayesian inference using low discrepancy sequences
… number of hyperparameters. The grid is a type of quasi-Monte Carlo (QMC) point set. Low discrepancy sequences (LDS) are QMC point sets that are well known to have significant advantages over grids in terms of convergence and accuracy, and suffer less from the so-called curse of dimensionality. …
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Scenario Analysis of Profitability through Simulation of Different Business Contract Models
… the uncertainty in demand parameters via a quasi-Monte Carlo simulator. The result is a set of visualizations that can be used to analyze both models under both deterministic and stochastic scenarios. The most influential factors in profitability stem from the state-mandated reimbursement …
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Stationary Density Computation of the Frobenius-Perron Operators Based on the Dirac Delta Function
… of fixed density functions we use the quasi- Monte Carlo method. We partition [0,1] into <em>n</em> subintervals, and for each subinterval we take <em>N</em> equal distance test points. Numerical results are given for several one dimensional test mappings.</p>
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Approximation of random/stochastic partial differential equations
… shells, and its applications to PDE problems; quasi-Monte Carlo (QMC) methods for a class of PDEs with random coefficients; and a discretisation for the solution of stochastic PDEs. First, we consider Gaussian random fields on spherical shells that are radially anisotropic and rotationally …
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Bayesian robust optimisation of buckling loads of trusses with random imperfections
… with random imperfections. Secondly, we adopt quasi-Monte Carlo sampling, i.e. Sobol sampling, to generate samples with better uniformity thanpseudorandom samples, which significantly reduces the number of samples and finite element evaluations thus further improving efficiency. Thirdly, we …