Latin hypercube sampling simulation full#

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. We propose a methodology that combines both the Monte Carlo simulation and the Latin hypercube sampling as follows. A discrete, Monte Carlo model of epidemics of influenzavirus infections in a human community is used for illustrative purposes.

Latin hypercube sampling simulation 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 sample.

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. of essence, stochastic simulation methods can offer sev. Any of the two-dimensional projections is still a Latin hypercube design with the same p15 points (although, for this particular case, the x 1 x 2 projection is the best in terms of space lling). Latin hypercube sampling that introduces a minimal performance loss compared to the original. Figure 3(a) illustrates the case of a Latin hypercube design with d3 dimensions and p15 points.

Discrete, algorithmic simulation and Monte Carlo methodologies are currently used in population biology, connectionist cognitive modeling, and physics. Latin hypercube samples are non-collapsing.