Suchergebnisse - "Bayesian mixture modeling"

Tracking sources and transformations of dissolved sulfate in karstic sub-basins in Southwest China using dual isotopes and water chemistry

Autoren: Jijuan Wang Zhongfa Zhou Hui Dong et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-13 (2025)

Schlagwörter: Karst sub-watersheds, Sulfate sources, Sulfur isotopes, Bayesian mixture modeling, Pollution source, Gui zhou, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/1407d2c1e00d4189b8a65263af5a40b3

Examining Prenatal Dietary Factors in Association with Child Autism-Related Traits Using a Bayesian Mixture Approach: Results from 2 United States Cohorts

Autoren: Lyall, Kristen Rando, Juliette Wang, Siwen et al.

Quelle: Current Developments in Nutrition. 7(8)

Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3210 Nutrition and Dietetics (for-2020), Pediatric (rcdc), Autism (rcdc), Brain Disorders (rcdc), Mental Health (rcdc), Complementary and Integrative Health (rcdc), Women's Health (rcdc), Clinical Research (rcdc), Dietary Supplements (rcdc), Nutrition (rcdc), Intellectual and Developmental Disabilities (IDD) (rcdc), Prevention (rcdc), 2.1 Biological and endogenous factors (hrcs-rac), 3.3 Nutrition and chemoprevention (hrcs-rac), Cardiovascular (hrcs-hc), autism, diet, Social Responsiveness Scale, Bayesian mixture modeling, EARLI, NHSII, Bayesian mixture modeling, EARLI, NHSII, Social Responsiveness Scale, autism, diet, 3003 Animal production (for-2020), 3006 Food sciences (for-2020), 3210 Nutrition and dietetics (for-2020)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/464310bb
https://escholarship.org/content/qt464310bb/qt464310bb.pdf

Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland

Autoren: Hussein Amjad Kadhem Safaa K.

Quelle: Open Engineering, Vol 12, Iss 1, Pp 204-214 (2022)

Schlagwörter: rainfall amounts, cumulative distribution, posterior predictive distribution, bayesian mixture modeling, prediction interval, TA1-2040, 0101 mathematics, Engineering (General). Civil engineering (General), 01 natural sciences, 0105 earth and related environmental sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://usir.salford.ac.uk/id/eprint/63589/1/10.1515_eng-2022-0024.pdf
https://doaj.org/article/bb6609334d644fc4a433d35103acc11a

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Autoren: Rad, Najmeh Nakhaei Bekker, Andriette, 1958 Arashi, Mohammad

Quelle: Sci Rep
Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022)

Schlagwörter: Wind energy production, Energy-Generating Resources, Mathematics and computing, Science, Bayes Theorem, Wind, 02 engineering and technology, 7. Clean energy, 01 natural sciences, Article, Wind direction prediction, Physical Phenomena, South Africa, Wind energy hotspots, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Bayesian mixture modeling, South Africa (SA), 0101 mathematics, Climate sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/35794177
https://doaj.org/article/5155f45541a444b1b5d6e14715b5a53a

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Autoren: Guha, Sharmistha Guhaniyogi, Rajarshi

Schlagwörter: Bayesian mixture modeling, Network clustering, Network node selection, Spike and slab prior, Brain connectome data

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/1969.1/197604

Verfügbarkeit: https://hdl.handle.net/1969.1/197604

Atlantic salmon Baltic Sea Catch_2006_2013_BMM.xlsx

Autoren: Marja-Liisa Koljonen (9017396) Michele Masuda (573861) Irma Kallio-Nyberg (9114684) et al.

Schlagwörter: Marine Biology, Atlantic salmon, genetics, Bayesian mixture modeling

Relation: https://figshare.com/articles/dataset/Atlantic_salmon_Baltic_Sea_Catch_2006_2013_BMM_xlsx/12594833

Verfügbarkeit: https://doi.org/10.6084/m9.figshare.12594833.v1

Bayesian Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Autoren: Guha, Sharmistha Guhaniyogi, Rajarshi

Schlagwörter: Bayesian mixture modeling, Network clustering, Network node selection, Spike and slab prior, Brain connectome data, stat, manag

Relation: https://hdl.handle.net/1969.1/196708

Verfügbarkeit: https://hdl.handle.net/1969.1/196708

Impact of regionally transported biomass burning on carbonaceous aerosol characterization, contribution and degradation in Pu'er, Southwest China.

Autoren: Shi J Rao W Zhao C et al.

Quelle: Journal of environmental sciences (China) [J Environ Sci (China)] 2025 Dec; Vol. 158, pp. 710-723. Date of Electronic Publication: 2025 Jan 27.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: IOS Press Country of Publication: Netherlands NLM ID: 100967627 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1001-0742 (Print) Linking ISSN: 10010742 NLM ISO Abbreviation: J Environ Sci (China) Subsets: MEDLINE

MeSH-Schlagworte: Air Pollutants*/analysis , Environmental Monitoring*, Aerosols/analysis ; China ; Biomass ; Particulate Matter/analysis ; Carbon/analysis ; Fires ; Air Pollution/statistics & numerical data ; Glucose/analogs & derivatives ; Glucose/analysis

Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data.

Autoren: Büschken J Böckenholt U Otter T et al.

Quelle: Psychometrika [Psychometrika] 2022 Jun; Vol. 87 (2), pp. 620-665. Date of Electronic Publication: 2021 Oct 26.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Cambridge University Press Country of Publication: United States NLM ID: 0376503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1860-0980 (Electronic) Linking ISSN: 00333123 NLM ISO Abbreviation: Psychometrika Subsets: MEDLINE

MeSH-Schlagworte: Attitude* , Eye-Tracking Technology*, Bayes Theorem ; Psychometrics ; Surveys and Questionnaires

Examining gene-environment interactions in comorbid depressive and disruptive behavior disorders using a Bayesian approach.

Autoren: Adrian M Kiff C Glazner C et al.

Quelle: Journal of psychiatric research [J Psychiatr Res] 2015 Sep; Vol. 68, pp. 125-33. Date of Electronic Publication: 2015 Jun 16.

Publikationsart: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: Pergamon Press Country of Publication: England NLM ID: 0376331 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1379 (Electronic) Linking ISSN: 00223956 NLM ISO Abbreviation: J Psychiatr Res Subsets: MEDLINE

MeSH-Schlagworte: Bayes Theorem* , Gene-Environment Interaction*, Depression/*epidemiology , Depression/*genetics , Mental Disorders/*epidemiology , Mental Disorders/*genetics, Adolescent ; Child ; Comorbidity ; Epistasis, Genetic ; Family/psychology ; Female ; Genetic Association Studies ; Genotyping Techniques ; Humans ; Logistic Models ; Male ; Retrospective Studies