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 language | English (US) |
|---|---|
| Pages (from-to) | 433-448 |
| Number of pages | 16 |
| Journal | Environmetrics |
| Volume | 24 |
| Issue number | 7 |
| DOIs | |
| State | Published - Nov 2013 |
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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 journal › Article
}
TY - JOUR
T1 - Reversible jump MCMC for inference in a deterministic individual-based model of tree growth for studying forest dynamics
AU - Gemoets, D.
AU - Barber, Jarrett
AU - Ogle, Kiona
PY - 2013/11
Y1 - 2013/11
N2 - 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.
AB - 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.
KW - Bayesian computation
KW - Data-model discordance
KW - Markov chain Monte Carlo
KW - Temporal misalignment
KW - Tree growth model
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U2 - 10.1002/env.2239
DO - 10.1002/env.2239
M3 - Article
AN - SCOPUS:84888034593
VL - 24
SP - 433
EP - 448
JO - Environmetrics
JF - Environmetrics
SN - 1180-4009
IS - 7
ER -