Journal Article

Propagating probability distributions of stand variables using sequential Monte Carlo methods

Jeffrey H. Gove

in Forestry: An International Journal of Forest Research

Published on behalf of Institute of Chartered Foresters

Volume 82, issue 4, pages 403-418
Published in print October 2009 | ISSN: 0015-752X
Published online May 2009 | e-ISSN: 1464-3626 | DOI: https://dx.doi.org/10.1093/forestry/cpp009
Propagating probability distributions of stand variables using sequential Monte Carlo methods

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  • Conservation of the Environment (Environmental Science)
  • Environmental Sustainability
  • Plant Sciences and Forestry

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A general probabilistic approach to stand yield estimation is developed based on sequential Monte Carlo filters, also known as particle filters. The essential steps in the development of the sampling importance resampling (SIR) particle filter are presented. The SIR filter is then applied to simulated and observed data showing how the ‘predictor–corrector’ scheme employed leads to a general probabilistic mechanism for updating growth model predictions with new observations. The method is applicable to decision making under uncertainty, where uncertainty is found in both model predictions and inventory observations.

Journal Article.  8302 words.  Illustrated.

Subjects: Conservation of the Environment (Environmental Science) ; Environmental Sustainability ; Plant Sciences and Forestry

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