# Interval Monte Carlo as an Alternative to Second-Order Sampling for Estimating Ecological Risk

Interval Monte Carlo as an Alternative to Second-Order Sampling for Estimating Ecological Risk Interval Monte Carlo offers an alternative to second-order approaches for modeling measurement uncertainty in a simulation framework. Using the example of computing quasi-extinction decline risk for an ecological population, an interval Monte Carlo model is built. If the model is not written optimally, the mean and standard deviation of the growth rate repeat, then the bounds on the quasi-extinction risk will be sub-optimal. Depending on your operational definition of what an interval is, the sub-optimal bounds may be the best possible bounds. A comparison between second-order and interval Monte Carlo is made, which reveals that second-order approaches can underestimate the upper bound on the quasi-extinction decline risk to the population when there are a large number of parameters that need to be sampled. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

# Interval Monte Carlo as an Alternative to Second-Order Sampling for Estimating Ecological Risk

, Volume 13 (1) – Nov 22, 2006
11 pages

/lp/springer_journal/interval-monte-carlo-as-an-alternative-to-second-order-sampling-for-XO0pM8x0n2
Publisher
Subject
Mathematics; Numeric Computing; Mathematical Modeling and Industrial Mathematics; Approximations and Expansions; Computational Mathematics and Numerical Analysis
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1007/s11155-006-9019-0
Publisher site
See Article on Publisher Site

### Abstract

Interval Monte Carlo offers an alternative to second-order approaches for modeling measurement uncertainty in a simulation framework. Using the example of computing quasi-extinction decline risk for an ecological population, an interval Monte Carlo model is built. If the model is not written optimally, the mean and standard deviation of the growth rate repeat, then the bounds on the quasi-extinction risk will be sub-optimal. Depending on your operational definition of what an interval is, the sub-optimal bounds may be the best possible bounds. A comparison between second-order and interval Monte Carlo is made, which reveals that second-order approaches can underestimate the upper bound on the quasi-extinction decline risk to the population when there are a large number of parameters that need to be sampled.

### Journal

Reliable ComputingSpringer Journals

Published: Nov 22, 2006

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