Author

Pu-Long Ma

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Preprint Oct 2026

Bayesian Soft-Tree Poisson Process Model for Covariate-Dependent Point Patterns

Estimating covariate-dependent intensity functions from spatial point pattern is challenging when the integrated intensity function cannot be evaluated analytically. This challenge becomes even worse in Bayesian inference due to repeated integration of the intensity function over the high-dimensional covariate space. T...

Pu-Long Ma · 0 citations

Open access Oct 2026

A Sequential Monte Carlo Method for Bayesian Poisson Process Trees

Statistical analysis of spatial point patterns often needs to deal with heterogeneous structures in intensity function estimation with possibly high‐dimensional covariate space. Likelihood based inference for spatial point process models needs to address the challenges in evaluating the multidimensional integral of...

Pu-Long Ma · Stat · 1 citation

Preprint Oct 2026

A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data

With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains. Existing Gaussian process (GP) models and computational strategies are often inadequate for analyzing such datasets in multi-dimensional domain...

Pu-Long Ma, Li Ma · 0 citations