![]() Voigt, M.: Probabilistische simulation des strukturmechanischen verhaltens von turbinenschaufeln. Liefvendahl, M., Stocki, R.: A study on algorithms for optimization of Latin hypercubes. In: Inference Control in Statistical Databases, Springer, pp. In: Proceedings of the 12th International Probabilistic Workshop, Weimar (2014)ĭandekar, R.A., Cohen, M., Kirkendall, N.: Sensitive micro data protection using Latin hypercube sampling technique. Schmidt, R., Voigt, M., Vogeler, K.: Extension of Latin hypercube samples while maintaining the correlation structure. Huntington, D.E., Lyrintzis, C.S.: Improvements to and limitations of Latin hypercube sampling. Sallaberry, C., Helton, J., Hora, S.: Extension of Latin hypercube samples with correlated variables. Vořechovský, M., Novák, D.: Correlation control in small-sample Monte Carlo type simulations I: a simulated annealing approach. Vořechovský, M.: Hierarchical refinement of Latin hypercube samples. In: 4th International Workshop on Reliable Engineering Computing (2010) Vořechovský, M.: Extension of sample size in Latin hypercube sampling with correlated variables. two methods are used: random sampling and Latin hypercube sampling. Tong, C.: Refinement strategies for stratified sampling methods. and evaluate Phase II Table 7 Proposed structure for a simulation report for a. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, University of Texas at San Antonio (2005) Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation.Pleming, J.B., Manteufel, R.D.: Replicated Latin hypercube sampling. Hu, Yuanman Chang, Yu Li, Xiuzhen Bu, Rencang. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaustive species age cohort information at each cell is not available. And finally, drag that cube along the w axis to create a 4D hypercube An interesting pattern can be observed here: a line had 2 points, a square had 4 lines, and a cube had 6 squares, so it follows that our tesseract will have 8 cubes. via to F(x) LHS simulation, generates n d1 where Fi samples (xi). Drag that square along the z axis to create a 3D cube. hypercube sampling We now discuss the method of Latin hypercube sampling. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. Drag that line along the y axis to create a 2D square. Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. It was developed in the 1970s and widely used in the simulation of. compared to improve the modeling speed of. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. 2.4.3.3 Latin hypercube sampling Latin hypercube sampling (LHS) is a method for. In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. Latin hypercube sampling is first compared with a common sampling procedure (simple random sampling) in LU decomposition simulation. ![]() In this study, we introduced an effective sampling method (Latin hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Monte Carlo type convenient for realisation of probabilistic. Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. ABSTRACT: The Latin Hypercube Sampling (LHS) method is a numerical simulation method of the. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |