@misc{c8efc44f-19d0-44d5-a7f8-86360decc257,
  abstract     = {{Spatial compositional data with exact zeros arise in many applications<br/>but remain challenging for models that often assume strictly positive<br/>components. We develop a Bayesian spatial model that combines the<br/>Dirichlet Composition Distribution, which accommodates exact zeros through<br/>component-specific zero probabilities, with a Gaussian Markov random field representation<br/>of the latent compositional field. The resulting framework supports<br/>joint inference on zero occurrence, compositional structure, and spatial dependence.<br/>For efficient posterior computation, we derive gradient-based expressions<br/>in additive log-ratio coordinates and investigate how MCMC performance depends<br/>on the update strategy for both the high-dimensional latent field and the<br/>parameters.<br/>We compare four latent-field samplers, MALA, pCN, gradient-informed pCN<br/>(gpCN), and the auxiliary-gradient sampler, combined with joint and decoupled<br/>updates for the Dirichlet precision parameter α, and random-walk Metropolis<br/>updates for the spatial range parameter κ.<br/>In a simulation study on a 35×35 grid with three compositional components<br/>and structural zeros, only the Q-preconditioned samplers, gpCN and auxiliarygradient, reliably recover the spatial structure. In addition, decoupling α via slice sampling is essential: joint updates lead to slow mixing in α and downward bias in κ, while decoupled updates recover the true parameters more accurately.<br/>We apply the best-performing strategy to the LANDCLIMII pollen-based REVEALS dataset for Europe, comprising 303 sites and 13 plant functional types<br/>with zero rates ranging from 0.3% to 78.2%. Ten-fold cross-validation confirms<br/>out-of-sample predictive performance, and stable parameter estimates across<br/>folds. These results show that accurate spatial inference for compositional data<br/>with exact zeros requires both a likelihood that respects zero structure and<br/>MCMC updates adapted to the geometry of the latent field.}},
  author       = {{Pirzamanbin, Behnaz}},
  language     = {{eng}},
  pages        = {{40--40}},
  title        = {{Bayesian inference for spatial compositional data with exact zeros using gradient-informed MCMC}},
  year         = {{2026}},
}