Accuracy and Speed in Monte Carlo Methods
Accuracy issues of Monte-Carlo methods for valuing American options
... In practice we have two options for the control variates for the given N independent replications. The first is to use N 1 (typically N 1 N ) simulations for estimating β min and the other N − N 1 sim- ulations for ...
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Monte Carlo methods
... In practice, either a reasonable choice for q is available, or not. The first case occurs when, e.g., π is almost unimodal and concentrates its mass on a small region of X . A Gaussian centered at this small region with ...
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Variational Monte Carlo methods
... , which means that the gradient needed for the so-called quantum force and local energy can be calculated analytically. This will speed up your code since the computation of the correlation part and the Slater ...
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Monte Carlo methods in derivative modelling
... A bank’s option book contains thousands of options which, to reduce computation time, practitioners want to price all at once. The payoffs of some options depends on the whole discrete sample path. Examples are average ...
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Monte-Carlo Methods for Risk Management
... problems the answer is that we don’t care. However, for many other other problems it is very important to know θ to a much greater level of accuracy. For example, suppose we are designing a nuclear power plant and ...
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Development of Monte Carlo Methods in Hypersonic Aerodynamics
... There is no clear stability condition for this method: if CFL > 1, then the particles can pass several cells, the algorithm remains the same, but its accuracy is lower. The condition imposed on ∆t is an ...
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Monte Carlo Methods in Quantitative Photoacoustic Tomography
... Approaches to remove system-dependent and fluence effects have been attempted by assuming that the internal fluence distribution does not change with oxygenation in a ves- sel of interest [131] or that the fluence is ...
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Distributional Monte Carlo Methods for the Boltzmann Equation
... particle methods (SPMs) for the Boltzmann equation, such as the Direct Simulation Monte Carlo (DSMC) technique, have gained popularity for the prediction of flows in which the assumptions behind the ...
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Variance Reduction Techniques in Monte Carlo Methods
... INTRODUCTION Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real ...computer speed has been increasing dramatically, ever since the ...
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Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems
... given accuracy, the cost of computing the posterior expectation with any of these Monte Carlo variants is proportional to the computational complexity of the same estimator for prior ...
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Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems
... given accuracy, the cost of computing the posterior expectation with any of these MC variants is proportional to the computational complexity of the same estimator for prior ...
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Advanced Monte Carlo Methods for Thermal Radiation Transport
... Implicit Monte Carlo Method and its Monte Carlo Interpretation The first step in most of the commonly-used methods to solve the nonlinear TRT equations is to linearize them over a time ...
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Pricing American Options using Monte Carlo Methods
... primary methods for pricing American options are binomial trees and other lattice methods, such as trinomial trees, and finite difference methods to solve the associated boundary value partial ...
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Monte Carlo Methods in Finance
... In figure 13.2, I show how the non-recombining tree model converges as a function of the number of steps to maturity for the pricing of European swaptions, and, more interestingly, in fi[r] ...
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Monte Carlo Methods in Finance
... Yiyang Yang (Advisor: Pr. Xiaolin Li and Pr. Zari Rachev) Monte Carlo Methods in Finance.. Xiaolin Li and Pr. Zari Rachev) Monte Carlo Methods in Finance.. Xiaolin Li and Pr. Zari Rachev[r] ...
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Sequential Monte Carlo Methods
... cn´ı Monte Carlo, ˇ c´ asticov´ e filtry, bayesovsk´ a indukce, optim´ aln´ı filtrace Abstract Monte Carlo methods provide recipe to convert numerical problems onto problems of random ...
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Kernel methods for Monte Carlo
... sequential Monte Carlo (SMC) methods are based on iterative importance sampling, and have traditionally been applied to inference in filtering problems with a sequence of time-varying target dis- ...
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Four essays on sequential Monte Carlo and quasi-Monte Carlo methods
... sequential Monte Carlo (SMC) ...Using Monte Carlo simulations, we provide strong evidence regarding the stat- istical performances of the SMC sampler as well as some new insights regarding the ...
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Comparing forecast accuracy: A Monte Carlo investigation
... The size and power properties of several tests of equal Mean Square Prediction Error (MSPE) and of Forecast Encompassing (FE) are evaluated, using Monte Carlo simulations, in the context of dynamic ...
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Monte Carlo Methods on Complex Networks
... In Chapter 3, the antiferromagnetic Potts model on complex networks is the subject of investigation. In our early work on this topic, we found that both local and cluster spin-flip algorithms fail to effectively sample ...
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