{"doi":"10.48550/arxiv.2003.10490","title":"Unknown","abstract":null,"journal":null,"year":null,"id":622262,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Approximate Bayesian inference for a spatial point process model\\n exhibiting regularity and random aggregation","abstract":"In this paper, we propose a doubly stochastic spatial point process model\\nwith both aggregation and repulsion. This model combines the ideas behind\\nStrauss processes and log Gaussian Cox processes. The likelihood for this model\\nis not expressible in closed form but it is easy to simulate realisations under\\nthe model. We therefore explain how to use approximate Bayesian computation\\n(ABC) to carry out statistical inference for this model. We suggest a method\\nfor model validation based on posterior predictions and global envelopes. We\\nillustrate the ABC procedure and model validation approach using both simulated\\npoint patterns and a real data example.\\n","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4287824571","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","oa_locations":[{"url":"https://arxiv.org/pdf/2003.10490","host_type":"repository"},{"url":"http://arxiv.org/abs/2003.10490","host_type":"repository"}],"fields_of_study":["Point processes and geometric inequalities","Economic and Environmental Valuation","Methodology (stat.ME)","FOS: Computer and information sciences"],"mesh_terms":[],"keywords":["Approximate Bayesian computation","Point process","Bayesian inference","Inference","Bayesian probability","Cox process","Computer science","Gaussian process","Computation","Statistical inference","Point (geometry)","Algorithm","Statistical model","Process (computing)","Statistical physics","Gaussian","Mathematics","Artificial intelligence","Statistics","Poisson process","Physics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T18:54:18.771651Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}