A UNIFIED APPROACH FOR FINITE-DIMENSIONAL, RARE-EVENT (2009)
R. G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters, Monte Carlo Simulation, Zhi Huang, ...
We consider the problem of estimating the small probability that a function of a finite number of random variables exceeds a large threshold. Each input random variable may be light-tailed or...
Emmanuel Benazera, Monte Carlo Simulation
Abstract — Current planning algorithms have difficulty handling the complexity that is due to an increase in domain uncertainty, and especially in the case of multi-dimensional continuous spaces....
Affected by Temperature and Initial Concentration of Spoilage Organisms (2008)
Monte Carlo Simulation, Pasteurized Milk As, Donald W. Schaffner, Jennifer Mcentire, Siobain Duffy, Rebecca Montville, ...
Predictive microbiology and quantitative microbial risk assessment are rapidly developing disciplines that use mathematical models to quantitatively estimate the presence and growth of microbes in...
K-1. Introduction. Monte Carlo simulation is a method of reliability analysis that should be used only when the system to be analyzed becomes too complex for use of simpler methods of reliability...
Multi-asset derivative pricing using quasi-random numbers (2008)
Monte Carlo Simulation, George Levy, Numerical Algorithms Group
In a previous Financial Engineering News article [1] the author gave introductory details concerning the use of quasi-random numbers for Monte Carlo simulation. The benefits to be gained by using...
Güzin Bayraksan, David P. Morton, Monte Carlo Simulation
Abstract: Wedevelop a procedure for testing the quality of a candidate solution for a class of stochastic programs. Quality is defined via the optimality gap and the procedure’s output is a...
Submitted to IEEE Transactions on Circuits and Systems in July 2003 (2008)
New Monte Carlo, Houssain Kettani, B. Ross Barmish, Monte Carlo Simulation
In this paper, we formulate a new type of Monte Carlo problem for circuits. Specific results are given for the class of resistive networks and open research problems are indicated for more general...
Springer Verlag, 1999 Reviewed for Metrika (2007)
Gibbs Elds, Monte Carlo Simulation, G. Winkler
Markov processes serve as mathematical models in applied sciences since the beginning of the 20th century. A main reason is their conceptual simplicity. Nevertheless, as a rst approximation to...
Simulation is a procedure in which random numbers are generated according to probabilities assumed to be associated with a source of uncertainty, such as a new product’s sales or, more...
Algorithm and Data Structures for Ecient Energy Maintenance during (2004)
Monte Carlo Simulation, Itay Lotan, Fabian Schwarzer, Dan Halperin, Jean-claude Latombe
Monte Carlo simulation (MCS) is a common methodology to compute pathways and thermodynamic properties of proteins. A simulation run is a series of random steps in conformation space, each perturbing...
com/archive/articles/0103ate.htm (2003)
S. Dey, E. J. Marinissen, Y. Zorian, Test Access Methodology, F. M. Bufler, Y. Asahi, ...
VIII. CONCLUSION In this paper, an efficient implementation of a TAM is proposed for the MSOC testing. The technique introduces I/O access of the analog cores through the MTAM switch, which is...
Abstract Reliability-based structural optimization using (2002)
Neural Networks, Monte Carlo Simulation, Manolis Papadrakakis, Nikos D. Lagaros
paper examines the application of neural networks (NN) to reliability-based structural optimization of largescale structural systems. The failure of the structural system is associated with the...
Monte Carlo Simulation, G. L. Drusano, J. P. Kleim, W. Prince, A. Bye
In order to choose a rational dose for GW 420867X, we first set a goal of therapy. We hypothesized that, for optimal antiretroviral activity, the trough free drug concentration should remain above...
Semiparametric Estimation of a Semilinear Censored Regression Model (1999)
S. Chen, S. Khan, Monte Carlo Simulation, Med Bias
> =2.50 c =2.75 c =3.00 c =3.25 c =3.50 n = 100: Mean Bias-0.2361-0.2349-0.2274-0.2267-0.2353-0.2463-0.2587-0.2731 Med. Bias-0.2256-0.2353-0.2269-0.2348-0.2323-0.2483-0.2662-0.2846 RMSE 0.8917...
Test of variational approximation for +4 quantum chain (1994)
Monte Carlo Simulation, Wolfhard Janke A, Tilman Sauer B, Communicated A. Lagendijk
We report results of a Monte Carlo simulation of the @4 quantum chain. In order to enhance the efficiency of the simulation we combine muItig~d simulation techniques with a refined discretization...