Application of Approximate Bayesian Computation algorithms for parameter estimation for Gibbs point processes based on partly missing data

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Publication Details

Journal
SPIRE - Sciences Po Institutional REpository
Published
2025-09-16
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
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article

Application of Approximate Bayesian Computation algorithms for parameter estimation for Gibbs point processes based on partly missing data

SPIRE - Sciences Po Institutional REpository
Markov Chains and Monte Carlo Methods
article

Application of Approximate Bayesian Computation algorithms for parameter estimation for Gibbs point processes based on partly missing data

article en

Abstract

No abstract available for this paper.
SPIRE - Sciences Po Institutional REpository
Institut Élie Cartan de Lorraine (FR), Chalmers University of Technology (SE), Université de Lorraine (FR)
Agence Nationale de la Recherche, Chalmers Tekniska Högskola, Division of Mathematical Sciences
Openalex Percentile: Top 43%
Markov Chains and Monte Carlo Methods
Open Access PDFOpenAlex
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Application of Approximate Bayesian Computation algorithms for parameter estimation for Gibbs point processes based on partly missing data · SPIRE - Sciences Po Institutional REpository (2025) | TGRS Research Map | TGRS