Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 87 for “"Importance sampling"”.
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Metody Importance Sampling při řešení optimalizačních úloh
… simulačních technik, jakými jsou Monte Carlo a Importance Sampling. Pro obě simulační techniky je zhotovena numerická studie jejich rozptylu a výkonnosti ve smyslu porovnání s optimálním řešením. Pro normální rozdělení s konkrétní střední hodnotou a rozptylem jsou empiricky odvozeny hodnoty …
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Kernel method in Monte Carlo importance sampling
… Carlo variance reduction technique called the Importance Sampling is presented. Since the efficiency of the importance sampling method depends primarily on the choice of the importance sampling density, the use of the kernel method to estimate the optimal importance sampling density is proposed.
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Importance sampling for reinforcement learning with multiple objectives
… reinforcement learning algorithms. We employ importance sampling (likelihood ratios) to achieve good performance in partially observable Nlarkov decision processes with few data. Our importance sampling estimator requires no knowledge about the environment and places few restrictions on the …
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Application of importance sampling simulation to CDMA systems
… This thesis studies the application of Importance Sampling to the simulation of the IS-95 CDMA standard. Importance Sampling techniques help to achieve the simulation results by sending fewer bits and thus reduce the simulation time by a significant factor. Different versions of …
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Importance sampling for LDPC codes and turbo-coded CDMA
… takes an unacceptably long time. We consider importance sampling (IS) schemes for the error rate estimation of LDPC codes, with the goal of dramatically reducing the necessary simulation time. In IS simulations, the sample distribution is biased to emphasize the occurrence of error events and …
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Probabilistic analysis of compression system stability using importance sampling
… is computed via a new approach based on Importance Sampling and a dynamic compression system model. In contrast to ordinary Monte Carlo methods Importance Sampling offers reduced confidence intervals, reduced number of samples and reduced model execution times. The new approach avoids …
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Performance evaluation and optimization of stochastic systems via importance sampling
… convergence properties of these algorithms. The Importance Sampling technique is employed to minimize the variance in estimating the system performance. A class of Importance Sampling biasing distributions is derived in this thesis for the specific use in analyzing single-user communication …
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Variational approximation for importance sampling and statistical inference on social influence
… problems. Social network analysis plays an importance role in many fields. In this dissertation, we focus on improving the efficiency of importance sampling, detecting the degrees of influence in networks, and exploring properties of generalized Erd\H{o}s-R\'enyi model. In the first part of …
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A coupling approach to rare event simulation via dynamic importance sampling
… constructs provably asymptotically efficient importance sampling estimators. Dynamic importance sampling is one these algorithms in which the choice of biasing distribution adapts in the course of a simulation according to the solution of an Isaacs partial differential equation or by solving a …
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The Markov chain Monte Carlo approach to importance sampling in stochastic programming
… in an optimization algorithm. We present an importance sampling framework for multistage stochastic programming that can produce accurate estimates of the recourse function using a fixed number of samples. Our framework uses Markov Chain Monte Carlo and Kernel Density Estimation algorithms to …
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Importance sampling simulation of free-space optical APD pulse position modulation receivers
… optical receiver. An improved technique for the importance sampling simulation of direct detection APD receivers has been developed. Two methods for efficiently simulating and biasing the probability distribution function of the APD process are presented and discussed. This is the first use the …
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Using importance sampling to simulate queuing networks with heavy-tailed service time distributions
… We develop a fast simulation method by using an importance sampling approach based on a change of measure of the service time in an M/G/1 queue. In particular, we present an algorithm for dynamically finding the optimal distribution within the parametrized class of delayed hazard rate twisted …
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Loaded dice in Monte Carlo : importance sampling in phase space integration and probability distributions for discrepancies
Contains fulltext : 18925.pdf (Publisher’s version ) (Open Access)
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Importance Resampling for Global Illumination
… form of Monte Carlo integration called Resampled Importance Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration for common rendering problems. We show how to …
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Network reliability estimation
… reliability estimation. There are two main sampling techniques in reliability estimation: combinatorial and permutational sampling. Combinatorial sampling has the advantage of speed but has poor performance in rare event simulations. Permutational sampling gives good simulation performance …
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Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables
We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, …
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Simulation-based approximate solution of large-scale linear least squares problems and applications
… and solution methods that use simulation, importance sampling, and low-dimensional calculations. The main components of this methodology are a regression/ regularization approach that can deal with nearly singular problems, and an importance sampling design approach that exploits existing …
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Accelerating Physically-Based Light Transport Algorithms
… parallel architectures and introduce intelligent importance sampling strategies which adapt themselves to image content in an unbiased manner. We demonstrate that these techniques offer substantial improvement over the prior art and offer sufficient generality to be deployed in many path …
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An exploration of improving sampling within Monte Carlo ray tracing using adaptive blue noise.
… we demonstrate that strategically choosing sampling points with an intelligent use of adaptive blue noise sampling methods can drastically reduce the computation time required in the rendering process. We explore the state of the art in blue noise sample generation and explore new ways it …
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Iterative Monte Carlo for quantum dynamics
… propagation with the features of Monte Carlo sampling. The stepwise evaluation of the path integral circumvents the growth of statistical error with time and the use of importance sampling leads to a favorable scaling of required grid points with the number of particles. Three different Monte …
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