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October 16, 2025Open Access

Bayesian inference for Neyman-Scott point processes with anisotropic clusters

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Authors

JDJiří DvořákEEEmily EwersTMTomáš Mrkvička

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Overview

Extended Bayesian MCMC methods improve parameter estimation and hypothesis testing in anisotropic point processes.

Key Points

  • The proposed Bayesian approach yields credible intervals for parameter estimates, effectively addressing anisotropy.
  • Simulation studies reveal the robustness of the method, particularly in estimating cluster parameters with varying orientations.
  • Credible intervals derived from posterior distributions facilitate significance testing for covariate dependencies.
  • The methodology allows examination of cluster orientation and elongation, enhancing the analysis of point processes.

Cite This Study

Dvořák et al. (2025) studied this question.

synapsesocial.com/papers/68f147cc724575985c3fd197https://doi.org/10.48550/arxiv.2505.09786
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