Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov models Article Swipe
Amina Shahzadi
,
Ting Wang
,
Matthew Parry
,
Mark Bebbington
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11634-025-00632-x
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11634-025-00632-x
Many point processes such as earthquakes or volcanic eruptions have incomplete records with the degree of incompleteness varying over time. For these point processes, the number of missing events between each pair of consecutively observed events can be a random variable that may depend on time, effecting the estimation of parameters or hazard. Such incomplete point processes can be modelled by compound renewal processes where the sum of renewal processes is a random variable because of random variable number of missing events. We propose shifted compound Poisson-Gamma and time-dependent shifted compound Poisson-Gamma renewal processes. Since the number of missing events can be regarded as an unobserved process, the proposed renewal processes are introduced to use in the framework of different types of homogeneous and inhomogeneous hidden Markov models to model the time-dependent variable number of missing events between each pair of consecutively observed events of incomplete point processes. Simulation experiments are employed to check the performance of proposed renewal processes with hidden Markov models. We apply the proposed models to the large magnitude explosive volcanic eruptions database to analyze the time-dependent incompleteness and demonstrate how we estimate the completeness of the record and the future hazard rate.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11634-025-00632-x
- https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409665347
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409665347Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s11634-025-00632-xDigital Object Identifier
- Title
-
Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-23Full publication date if available
- Authors
-
Amina Shahzadi, Ting Wang, Matthew Parry, Mark BebbingtonList of authors in order
- Landing page
-
https://doi.org/10.1007/s11634-025-00632-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdfDirect OA link when available
- Concepts
-
Hidden Markov model, Point (geometry), Computer science, Markov chain, Markov model, Statistical physics, Time point, Mathematics, Artificial intelligence, Machine learning, Physics, Geometry, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4409665347 |
|---|---|
| doi | https://doi.org/10.1007/s11634-025-00632-x |
| ids.doi | https://doi.org/10.1007/s11634-025-00632-x |
| ids.openalex | https://openalex.org/W4409665347 |
| fwci | 9.76450075 |
| type | article |
| title | Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov models |
| awards[0].id | https://openalex.org/G477611748 |
| awards[0].funder_id | https://openalex.org/F4320334317 |
| awards[0].display_name | |
| awards[0].funder_award_id | Doctral Scholarship |
| awards[0].funder_display_name | Division of Sciences, University of Otago |
| awards[1].id | https://openalex.org/G5806595181 |
| awards[1].funder_id | https://openalex.org/F4320335369 |
| awards[1].display_name | |
| awards[1].funder_award_id | UOO1419 |
| awards[1].funder_display_name | Marsden Fund |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11830 |
| topics[0].field.id | https://openalex.org/fields/26 |
| topics[0].field.display_name | Mathematics |
| topics[0].score | 0.9944999814033508 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2604 |
| topics[0].subfield.display_name | Applied Mathematics |
| topics[0].display_name | Point processes and geometric inequalities |
| topics[1].id | https://openalex.org/T12417 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9936000108718872 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2608 |
| topics[1].subfield.display_name | Geometry and Topology |
| topics[1].display_name | Morphological variations and asymmetry |
| topics[2].id | https://openalex.org/T11901 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9915000200271606 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Bayesian Methods and Mixture Models |
| funders[0].id | https://openalex.org/F4320310371 |
| funders[0].ror | https://ror.org/01jmxt844 |
| funders[0].display_name | University of Otago |
| funders[1].id | https://openalex.org/F4320334317 |
| funders[1].ror | |
| funders[1].display_name | Division of Sciences, University of Otago |
| funders[2].id | https://openalex.org/F4320335369 |
| funders[2].ror | |
| funders[2].display_name | Marsden Fund |
| is_xpac | False |
| apc_list.value | 2390 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2990 |
| apc_paid.value | 2390 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2990 |
| concepts[0].id | https://openalex.org/C23224414 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5726649165153503 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q176769 |
| concepts[0].display_name | Hidden Markov model |
| concepts[1].id | https://openalex.org/C28719098 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5246065855026245 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[1].display_name | Point (geometry) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4984931945800781 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C98763669 |
| concepts[3].level | 2 |
| concepts[3].score | 0.47565433382987976 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q176645 |
| concepts[3].display_name | Markov chain |
| concepts[4].id | https://openalex.org/C163836022 |
| concepts[4].level | 3 |
| concepts[4].score | 0.46113622188568115 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q6771326 |
| concepts[4].display_name | Markov model |
| concepts[5].id | https://openalex.org/C121864883 |
| concepts[5].level | 1 |
| concepts[5].score | 0.450725793838501 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[5].display_name | Statistical physics |
| concepts[6].id | https://openalex.org/C2779466056 |
| concepts[6].level | 2 |
| concepts[6].score | 0.44544756412506104 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q107630651 |
| concepts[6].display_name | Time point |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.30142050981521606 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.292991042137146 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.1942664384841919 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C121332964 |
| concepts[10].level | 0 |
| concepts[10].score | 0.14345911145210266 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[10].display_name | Physics |
| concepts[11].id | https://openalex.org/C2524010 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[11].display_name | Geometry |
| concepts[12].id | https://openalex.org/C24890656 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[12].display_name | Acoustics |
| keywords[0].id | https://openalex.org/keywords/hidden-markov-model |
| keywords[0].score | 0.5726649165153503 |
| keywords[0].display_name | Hidden Markov model |
| keywords[1].id | https://openalex.org/keywords/point |
| keywords[1].score | 0.5246065855026245 |
| keywords[1].display_name | Point (geometry) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.4984931945800781 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/markov-chain |
| keywords[3].score | 0.47565433382987976 |
| keywords[3].display_name | Markov chain |
| keywords[4].id | https://openalex.org/keywords/markov-model |
| keywords[4].score | 0.46113622188568115 |
| keywords[4].display_name | Markov model |
| keywords[5].id | https://openalex.org/keywords/statistical-physics |
| keywords[5].score | 0.450725793838501 |
| keywords[5].display_name | Statistical physics |
| keywords[6].id | https://openalex.org/keywords/time-point |
| keywords[6].score | 0.44544756412506104 |
| keywords[6].display_name | Time point |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.30142050981521606 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.292991042137146 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.1942664384841919 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/physics |
| keywords[10].score | 0.14345911145210266 |
| keywords[10].display_name | Physics |
| language | en |
| locations[0].id | doi:10.1007/s11634-025-00632-x |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210175730 |
| locations[0].source.issn | 1862-5347, 1862-5355 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1862-5347 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Advances in Data Analysis and Classification |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Advances in Data Analysis and Classification |
| locations[0].landing_page_url | https://doi.org/10.1007/s11634-025-00632-x |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5117247169 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0403-5919 |
| authorships[0].author.display_name | Amina Shahzadi |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Amina Shahzadi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5021459230 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4767-3777 |
| authorships[1].author.display_name | Ting Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ting Wang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5026967054 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6588-0219 |
| authorships[2].author.display_name | Matthew Parry |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Matthew Parry |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5005911653 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3504-7418 |
| authorships[3].author.display_name | Mark Bebbington |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Mark Bebbington |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11830 |
| primary_topic.field.id | https://openalex.org/fields/26 |
| primary_topic.field.display_name | Mathematics |
| primary_topic.score | 0.9944999814033508 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2604 |
| primary_topic.subfield.display_name | Applied Mathematics |
| primary_topic.display_name | Point processes and geometric inequalities |
| related_works | https://openalex.org/W1510894296, https://openalex.org/W2134386692, https://openalex.org/W2084326697, https://openalex.org/W2194396582, https://openalex.org/W2027903142, https://openalex.org/W2082284720, https://openalex.org/W2116722627, https://openalex.org/W2537260108, https://openalex.org/W2354322608, https://openalex.org/W2158700654 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1007/s11634-025-00632-x |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210175730 |
| best_oa_location.source.issn | 1862-5347, 1862-5355 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1862-5347 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Advances in Data Analysis and Classification |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Advances in Data Analysis and Classification |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s11634-025-00632-x |
| primary_location.id | doi:10.1007/s11634-025-00632-x |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210175730 |
| primary_location.source.issn | 1862-5347, 1862-5355 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1862-5347 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Advances in Data Analysis and Classification |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Advances in Data Analysis and Classification |
| primary_location.landing_page_url | https://doi.org/10.1007/s11634-025-00632-x |
| publication_date | 2025-04-23 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2142635246, https://openalex.org/W4388561389, https://openalex.org/W4253739140, https://openalex.org/W2086699924, https://openalex.org/W2011189323, https://openalex.org/W2089455726, https://openalex.org/W1979249479, https://openalex.org/W4388897873, https://openalex.org/W1993149050, https://openalex.org/W2087348129, https://openalex.org/W2757164635, https://openalex.org/W2114151630, https://openalex.org/W2161763932, https://openalex.org/W2479761636, https://openalex.org/W2493226118, https://openalex.org/W2002022051, https://openalex.org/W2049633694, https://openalex.org/W4388062655, https://openalex.org/W2059586969, https://openalex.org/W2031123121, https://openalex.org/W2021949005, https://openalex.org/W2111623343, https://openalex.org/W1894982206, https://openalex.org/W2013666932, https://openalex.org/W2167235427, https://openalex.org/W2030520626, https://openalex.org/W2011277999, https://openalex.org/W2064057541, https://openalex.org/W1955543760, https://openalex.org/W2498094064, https://openalex.org/W6948238583, https://openalex.org/W2123906784, https://openalex.org/W2064758233, https://openalex.org/W2028944227, https://openalex.org/W2172192324, https://openalex.org/W1985514205, https://openalex.org/W2007152174, https://openalex.org/W2767557981, https://openalex.org/W2470089657, https://openalex.org/W2622738426, https://openalex.org/W271417089, https://openalex.org/W2619139687, https://openalex.org/W2510396088, https://openalex.org/W4296232810, https://openalex.org/W1971638622, https://openalex.org/W2168703246, https://openalex.org/W2905575691, https://openalex.org/W656022232, https://openalex.org/W110108909, https://openalex.org/W1973610876, https://openalex.org/W2034350490, https://openalex.org/W2028619795, https://openalex.org/W2980124824, https://openalex.org/W2053742104, https://openalex.org/W284777917, https://openalex.org/W4240912299, https://openalex.org/W3019826447 |
| referenced_works_count | 57 |
| abstract_inverted_index.a | 39, 72 |
| abstract_inverted_index.We | 83, 165 |
| abstract_inverted_index.an | 105 |
| abstract_inverted_index.as | 5, 104 |
| abstract_inverted_index.be | 38, 59, 102 |
| abstract_inverted_index.by | 61 |
| abstract_inverted_index.in | 116 |
| abstract_inverted_index.is | 71 |
| abstract_inverted_index.of | 16, 27, 33, 50, 68, 76, 80, 98, 119, 122, 135, 141, 145, 157, 190 |
| abstract_inverted_index.on | 45 |
| abstract_inverted_index.or | 7, 52 |
| abstract_inverted_index.to | 114, 129, 153, 170, 178 |
| abstract_inverted_index.we | 186 |
| abstract_inverted_index.For | 21 |
| abstract_inverted_index.and | 88, 124, 183, 193 |
| abstract_inverted_index.are | 112, 151 |
| abstract_inverted_index.can | 37, 58, 101 |
| abstract_inverted_index.how | 185 |
| abstract_inverted_index.may | 43 |
| abstract_inverted_index.sum | 67 |
| abstract_inverted_index.the | 14, 25, 48, 66, 96, 108, 117, 131, 155, 167, 171, 180, 188, 191, 194 |
| abstract_inverted_index.use | 115 |
| abstract_inverted_index.Many | 1 |
| abstract_inverted_index.Such | 54 |
| abstract_inverted_index.each | 31, 139 |
| abstract_inverted_index.have | 10 |
| abstract_inverted_index.over | 19 |
| abstract_inverted_index.pair | 32, 140 |
| abstract_inverted_index.such | 4 |
| abstract_inverted_index.that | 42 |
| abstract_inverted_index.with | 13, 161 |
| abstract_inverted_index.Since | 95 |
| abstract_inverted_index.apply | 166 |
| abstract_inverted_index.check | 154 |
| abstract_inverted_index.large | 172 |
| abstract_inverted_index.model | 130 |
| abstract_inverted_index.point | 2, 23, 56, 147 |
| abstract_inverted_index.rate. | 197 |
| abstract_inverted_index.these | 22 |
| abstract_inverted_index.time, | 46 |
| abstract_inverted_index.time. | 20 |
| abstract_inverted_index.types | 121 |
| abstract_inverted_index.where | 65 |
| abstract_inverted_index.Markov | 127, 163 |
| abstract_inverted_index.degree | 15 |
| abstract_inverted_index.depend | 44 |
| abstract_inverted_index.events | 29, 36, 100, 137, 144 |
| abstract_inverted_index.future | 195 |
| abstract_inverted_index.hazard | 196 |
| abstract_inverted_index.hidden | 126, 162 |
| abstract_inverted_index.models | 128, 169 |
| abstract_inverted_index.number | 26, 79, 97, 134 |
| abstract_inverted_index.random | 40, 73, 77 |
| abstract_inverted_index.record | 192 |
| abstract_inverted_index.analyze | 179 |
| abstract_inverted_index.because | 75 |
| abstract_inverted_index.between | 30, 138 |
| abstract_inverted_index.events. | 82 |
| abstract_inverted_index.hazard. | 53 |
| abstract_inverted_index.missing | 28, 81, 99, 136 |
| abstract_inverted_index.models. | 164 |
| abstract_inverted_index.propose | 84 |
| abstract_inverted_index.records | 12 |
| abstract_inverted_index.renewal | 63, 69, 93, 110, 159 |
| abstract_inverted_index.shifted | 85, 90 |
| abstract_inverted_index.varying | 18 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.compound | 62, 86, 91 |
| abstract_inverted_index.database | 177 |
| abstract_inverted_index.employed | 152 |
| abstract_inverted_index.estimate | 187 |
| abstract_inverted_index.modelled | 60 |
| abstract_inverted_index.observed | 35, 143 |
| abstract_inverted_index.process, | 107 |
| abstract_inverted_index.proposed | 109, 158, 168 |
| abstract_inverted_index.regarded | 103 |
| abstract_inverted_index.variable | 41, 74, 78, 133 |
| abstract_inverted_index.volcanic | 8, 175 |
| abstract_inverted_index.different | 120 |
| abstract_inverted_index.effecting | 47 |
| abstract_inverted_index.eruptions | 9, 176 |
| abstract_inverted_index.explosive | 174 |
| abstract_inverted_index.framework | 118 |
| abstract_inverted_index.magnitude | 173 |
| abstract_inverted_index.processes | 3, 57, 64, 70, 111, 160 |
| abstract_inverted_index.Simulation | 149 |
| abstract_inverted_index.estimation | 49 |
| abstract_inverted_index.incomplete | 11, 55, 146 |
| abstract_inverted_index.introduced | 113 |
| abstract_inverted_index.parameters | 51 |
| abstract_inverted_index.processes, | 24 |
| abstract_inverted_index.processes. | 94, 148 |
| abstract_inverted_index.unobserved | 106 |
| abstract_inverted_index.demonstrate | 184 |
| abstract_inverted_index.earthquakes | 6 |
| abstract_inverted_index.experiments | 150 |
| abstract_inverted_index.homogeneous | 123 |
| abstract_inverted_index.performance | 156 |
| abstract_inverted_index.completeness | 189 |
| abstract_inverted_index.Poisson-Gamma | 87, 92 |
| abstract_inverted_index.consecutively | 34, 142 |
| abstract_inverted_index.inhomogeneous | 125 |
| abstract_inverted_index.incompleteness | 17, 182 |
| abstract_inverted_index.time-dependent | 89, 132, 181 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.9308137 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |