#Latin hypercube vs monte carlo full#

Geostatistical techniques include ordinary kriging, simple kriging, universal kriging, probability kriging, indicator kriging and disjunctive kriging. Deterministic techniques include polynomial, inverse distance weighted, and radial based functions for interpolation. There are two main groups of interpolation techniques: deterministic and geostatistical (ESRI, 2001). Thus, interpolation or extrapolation is often necessary to derive species and related data for cells where information is missing, based on the inventory data. This is especially true when the simulated area has millions of cells. However, forest inventory data is often sparsely distributed across the landscape and no forest inventory can provide all the information at the cell level. Each cell requires the input of dominant canopy tree species, secondary tree species and/or other species related information (e.g. Most of these models employ a raster data format and the forest landscape is conceptualized as a grid of equal-sized cells or sites. In the last decade many spatially explicit forest models have been developed to simulate forest landscape changes (e.g. 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. 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. 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. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. 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. Although the FF scheme used over 14 times as many samples as the LH sampling one, both provided comparable predictive ability and comparable information about simulation sensitivity to model features.Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. A discrete, Monte Carlo model of epidemics of influenzavirus infections in a human community is used for illustrative purposes.

#Latin hypercube vs monte carlo full#

In this article, a sensitivity study based on a Latin hypercube (LH) sampling design is compared with a similar study using a full factorial (FF), fixed-point smaple. To include more model features and their interactions in a sensitivity study, while limiting computer utilization, various sampling methods have been suggested. Sometimes, one or two parameters are modified at a time in an ad hoc fashion in an attempt to assess sensitivity. Traditional methods of sensitivity analysis for systems of differential equations do not apply. However, little is typically known about the sensitivity of such models to changes in the values of the model features. Discrete, algorithmic simulation and Monte Carlo methodologies are currently used in population biology, connectionist cognitive modeling, and physics.