The Experts below are selected from a list of 75624 Experts worldwide ranked by ideXlab platform
Hans-andrea Loeliger - One of the best experts on this subject based on the ideXlab platform.
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Monte Carlo algorithms for the partition function and information rates of two dimensional channels
IEEE Transactions on Information Theory, 2013Co-Authors: Mehdi Molkaraie, Hans-andrea LoeligerAbstract:The paper proposes Monte Carlo algorithms for the computation of the information rate of 2-D source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noiseless capacity, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo Estimate of a partition function. The proposed algorithms use tree-based Gibbs sampling and multilayer (multitemperature) importance sampling. The viability of the proposed algorithms is demonstrated by simulation results.
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Monte Carlo algorithms for the partition function and information rates of two dimensional channels
arXiv: Information Theory, 2011Co-Authors: Mehdi Molkaraie, Hans-andrea LoeligerAbstract:The paper proposes Monte Carlo algorithms for the computation of the information rate of two-dimensional source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noiseless capacity, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo Estimate of a partition function. The proposed algorithms use tree-based Gibbs sampling and multilayer (multitemperature) importance sampling. The viability of the proposed algorithms is demonstrated by simulation results.
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Estimating the partition function of 2-D fields and the capacity of constrained noiseless 2-D channels using tree-based Gibbs sampling
2009 IEEE Information Theory Workshop, 2009Co-Authors: Hans-andrea Loeliger, Mehdi MolkaraieAbstract:Tree-based Gibbs sampling (proposed by Hamze and de Freitas) is used to compute a Monte-Carlo Estimate of the partition function of factor graphs with cycles. The proposed method can be used, in particular, to compute the capacity of noiseless constrained 2-D channels.
G. A. Mikhailov - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters
Doklady Mathematics, 2018Co-Authors: G. A. MikhailovAbstract:Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo Estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.
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Modifications of the standard vector Monte Carlo Estimate for characteristics analysis of scattered polarized radiation
Doklady Mathematics, 2017Co-Authors: G. A. Mikhailov, Sergei M. Prigarin, S. A. RozhenkoAbstract:There are two versions of weighted vector algorithms for the statistical modeling of polarized radiative transfer: a “standard” one, which is convenient for parametric analysis of results, and an “adaptive” one, which ensures finite variances of Estimates. The application of the adaptive algorithm is complicated by the necessity of modeling the previously unknown transition density. An optimal version of the elimination algorithm used in this case is presented in this paper. A new combined algorithm with a finite variance and an algorithm with a mixed transition density are constructed. The comparative efficiency of the latter is numerically studied as applied to radiative transfer with a molecular scattering matrix.
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Monte Carlo Estimate of backscattering noise asymptotics parameters with allowance for polarization
Atmospheric and Oceanic Optics, 2011Co-Authors: G. A. Mikhailov, N. V. Tracheva, S. A. UkhinovAbstract:We Estimate parameters of time asymptotics of the polarized radiation flow emitting from a semi-infinite layer of the scattering and absorbing substance illuminated by an external directed source. Calculations on a multiprocessor cluster demonstrate that, in this case, polarization has no effect on parameters of the asymptotics of reflected radiation determining the “backscattering noise” in optical sensing. For bounded media, parameters of the polarized and nonpolarized radiation asymptotics are different, depending on the size of the transfer region; i.e., depolarization of the radiation flow is slightly delayed relative to the passage to asymptotics.
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Variance of a Standard Vector Monte Carlo Estimate in the Theory of Polarized Radiative Transfer
Computational Mathematics and Mathematical Physics, 2006Co-Authors: G. A. Mikhailov, S. A. Ukhinov, A. S. ChimaevaAbstract:The spectral radius ρ of the matrix integral operator defining the covariance matrix of a standard vector Monte Carlo Estimate in the polarized radiative transfer problem is examined. The theory of positive operators is used to analytically calculate ρ = ρ0 for transfer through an infinite homogeneous medium. For a bounded medium, it is shown that ρ is approximately equal to ρ0 times the spectral radius of the operator corresponding to radiative transfer without polarization. This is shown numerically by estimating the iterations of the corresponding resolvent and approximately analytically by using a perturbation of a special functional.
Karl Breitung - One of the best experts on this subject based on the ideXlab platform.
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the geometry of limit state function graphs and subset simulation counterexamples
Reliability Engineering & System Safety, 2019Co-Authors: Karl BreitungAbstract:Abstract In the last fifteen years the subset sampling method has often been used in reliability problems as a tool for calculating small probabilities. This method is extrapolating from an initial Monte Carlo Estimate, for which the probability content of a failure domain found by a suitable higher level of the original limit state function. Then iteratively conditional probabilities are Estimated for failures domains decreasing to the original failure domain. However, there are implied premises, regarding the structure of the failure domains, which must be fulfilled for the method to work properly. The examples studied in this paper demonstrate that inaccurate results might be obtained if the said premises are not fulfilled. This demonstrates that there are limitations for the application of this method.
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the geometry of limit state function graphs and subset simulation
arXiv: Computation, 2017Co-Authors: Karl BreitungAbstract:In the last fifteen the subset sampling method has often been used in reliability problems as a tool for calculating small probabilities. This method is extrapolating from an initial Monte Carlo Estimate for the probability content of a failure domain found by a suitable higher level of the original limit state function. Then iteratively conditional probabilities are Estimated for failures domains decreasing to the original failure domain. But there are assumptions not immediately obvious about the structure of the failure domains which must be fulfilled that the method works properly. Here examples are studied that show that at least in some cases if these premises are not fulfilled, inaccurate results may be obtained. For the further development of the subset sampling method it is certainly desirable to find approaches where it is possible to check that these implicit assumptions are not violated. Also it would be probably important to develop further improvements of the concept to get rid of these limitations.
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Extrapolation, Invariance, Geometry and Subset Sampling
14th International Probabilistic Workshop, 2016Co-Authors: Karl BreitungAbstract:In the last years the subset sampling method has often been used in reliability problems as a tool for calculating very small probabilities. The method extrapolates from an initial Monte Carlo Estimate for the probability content of a failure domain found by a suitable higher level of the original limit state function. Then iteratively conditional probabilities are Estimated for values of the limit state function decreasing to zero. But there are implicit assumptions about the structure of the failure domains which have to be fulfilled that the method works properly. It is shown by examples that at least in some cases if these assumptions are not fulfilled, erroneous results may be obtained. For the further development of the subset sampling concept it might be desirable to find approaches where it is possible to ascertain that these implicit assumptions are not violated or how to avoid by an increased computational effort misleading influences of the structure of the limit state functions.
Mehdi Molkaraie - One of the best experts on this subject based on the ideXlab platform.
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Monte Carlo algorithms for the partition function and information rates of two dimensional channels
IEEE Transactions on Information Theory, 2013Co-Authors: Mehdi Molkaraie, Hans-andrea LoeligerAbstract:The paper proposes Monte Carlo algorithms for the computation of the information rate of 2-D source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noiseless capacity, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo Estimate of a partition function. The proposed algorithms use tree-based Gibbs sampling and multilayer (multitemperature) importance sampling. The viability of the proposed algorithms is demonstrated by simulation results.
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Monte Carlo algorithms for the partition function and information rates of two dimensional channels
arXiv: Information Theory, 2011Co-Authors: Mehdi Molkaraie, Hans-andrea LoeligerAbstract:The paper proposes Monte Carlo algorithms for the computation of the information rate of two-dimensional source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noiseless capacity, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo Estimate of a partition function. The proposed algorithms use tree-based Gibbs sampling and multilayer (multitemperature) importance sampling. The viability of the proposed algorithms is demonstrated by simulation results.
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Estimating the partition function of 2-D fields and the capacity of constrained noiseless 2-D channels using tree-based Gibbs sampling
2009 IEEE Information Theory Workshop, 2009Co-Authors: Hans-andrea Loeliger, Mehdi MolkaraieAbstract:Tree-based Gibbs sampling (proposed by Hamze and de Freitas) is used to compute a Monte-Carlo Estimate of the partition function of factor graphs with cycles. The proposed method can be used, in particular, to compute the capacity of noiseless constrained 2-D channels.
Ricci R Bitti - One of the best experts on this subject based on the ideXlab platform.
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a Monte Carlo Estimate of crystallite size and microstrain distribution functions from x ray line broadening
Journal of Applied Crystallography, 1995Co-Authors: P E Di Nunzio, S Martelli, Ricci R BittiAbstract:A method of X-ray line-broadening analysis is presented whereby the coherence length and microstrain contributions can be calculated using a Monte Carlo interference-function-fitting algorithm. The method is based on the `column-like' crystal model and can be applied to both single and multiple-order reflections. Examples on simulated diffraction peaks, deformed face-centred-cubic palladium powder and heavily textured deformed body-centred-cubic low-carbon-steel sheets are presented and comparisons are made with currently applied methods.