Reversible jump MCMC for inference in a deterministic individual-based model of tree growth for studying forest dynamics

D. Gemoets, Jarrett Barber, Kiona Ogle

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Scientists use deterministic models to study and forecast the behavior of complex environmental processes, with increasing emphasis on incorporating data to inform model input parameters and accounting for parameter uncertainty. We work with a deterministic, individual-based model (IBM) of tree growth and mortality, which is under development to explore forest dynamics. Some values of IBM input parameters cause premature virtual tree mortality relative to the actual mortality status of an observed tree. This discordance in mortality causes dimension changes in the state of a stochastic implementation of IBM outputs and leads us to address trans-dimensional moves among states with a novel formulation of reversible jump Markov chain Monte Carlo (RJMCMC). In particular, we present an RJMCMC algorithm that uses a continuously supported, multidimensional index-the IBM input parameter-instead of a discrete index typical of model determination applications. We use both synthetic data and data from the Forest Inventory and Analysis database representing two tree species. We compare results for each dataset and species between our reversible jump (RJ) specification and an alternative, non-RJ specification. The RJ formulation compares favorably to the non-RJ formulation with regard to achieving convergence and yielding biologically realistic IBM input parameter estimates.

Original languageEnglish (US)
Pages (from-to)433-448
Number of pages16
JournalEnvironmetrics
Volume24
Issue number7
DOIs
StatePublished - Nov 2013

Fingerprint

Reversible Jump MCMC
Individual-based Model
individual-based model
forest dynamics
Deterministic Model
Mortality
Reversible Jump
Reversible Jump Markov Chain Monte Carlo
mortality
Markov chain
Formulation
Jump
Specification
Markov Chain Monte Carlo Algorithms
forest inventory
Parameter Uncertainty
Synthetic Data
Forecast
parameter
Output

Keywords

  • Bayesian computation
  • Data-model discordance
  • Markov chain Monte Carlo
  • Temporal misalignment
  • Tree growth model

ASJC Scopus subject areas

  • Ecological Modeling
  • Statistics and Probability

Cite this

Reversible jump MCMC for inference in a deterministic individual-based model of tree growth for studying forest dynamics. / Gemoets, D.; Barber, Jarrett; Ogle, Kiona.

In: Environmetrics, Vol. 24, No. 7, 11.2013, p. 433-448.

Research output: Contribution to journalArticle

Gemoets, D. ; Barber, Jarrett ; Ogle, Kiona. / Reversible jump MCMC for inference in a deterministic individual-based model of tree growth for studying forest dynamics. In: Environmetrics. 2013 ; Vol. 24, No. 7. pp. 433-448.
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