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Abstract Details

Activity Number: 41
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #305258
Title: Deriving Distributions via Simulation: Easy Ways to Incorporate Hard Distributions
Author(s): Megan D. Higgs*+
Companies: Montana State University
Address: 1146 S Pinecrest Drive, Bozeman, MT, 59715-5941, United States
Keywords: abundance ; Bayesian ; mark-resight ; Monte Carlo
Abstract:

Approximating a data-generating mechanism with easily available distributions is often challenging in the face of non-traditional and complicated study designs and/or analytical difficulties. However, it may be possible to derive needed distributions using simulation, bypassing the need for unrealistic assumptions or more sophisticated analytical results. Monte Carlo simulation based derivations of probability distributions can be used to incorporate otherwise difficult distributions within a larger modeling framework. I provide two examples where I trade analytically derived distributions for those derived via simulation. In both cases, the distributions are then used to obtain draws from complete conditional distributions within a Gibbs sampling algorithm. I focus my discussion on estimation of population size in ecological settings, though the idea is general and has wide applicability across disciplines.


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