IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/dm467.html
   My bibliography  Save this paper

Mô phỏng hierarchy MCMC cho mô hình Satlns ~ end + ses + res + insured, BayesVL v0.6.5 trên 1042 quan sát thực

Author

Listed:
  • Vuong, Quan-Hoang
  • La, Viet-Phuong

Abstract

Bài viết này (Note 2b) trình bày một mô hình và ứng dụng mô phỏng BayesVL v0.6.5 [1] trên môi trường R và Stan MCMC, cho bài toán thực tế [2] với dữ liệu thực tế, gồm 1042 điểm dữ liệu [3]. Nội dung của bài là một phần của tài liệu do AISDL biên soạn phục vụ hướng dẫn sử dụng “The BayesVL R Package”, nhằm thúc đẩy việc sử dụng phương pháp thống kê Bayesian trong KHXH&NV.

Suggested Citation

  • Vuong, Quan-Hoang & La, Viet-Phuong, 2019. "Mô phỏng hierarchy MCMC cho mô hình Satlns ~ end + ses + res + insured, BayesVL v0.6.5 trên 1042 quan sát thực," OSF Preprints dm467, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:dm467
    DOI: 10.31219/osf.io/dm467
    as

    Download full text from publisher

    File URL: https://osf.io/download/5cc59921d7dc3f001969b789/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/dm467?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Roland R. Ramsahai, 2020. "Connecting actuarial judgment to probabilistic learning techniques with graph theory," Papers 2007.15475, arXiv.org.
    3. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    4. Myriam Patricia Cifuentes & Clara Mercedes Suarez & Ricardo Cifuentes & Noel Malod-Dognin & Sam Windels & Jose Fernando Valderrama & Paul D. Juarez & R. Burciaga Valdez & Cynthia Colen & Charles Phill, 2022. "Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    5. Silvia de Juan & Maria Dulce Subida & Andres Ospina-Alvarez & Ainara Aguilar & Miriam Fernandez, 2020. "Disentangling the socio-ecological drivers behind illegal fishing in a small-scale fishery managed by a TURF system," Papers 2012.08970, arXiv.org.
    6. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    7. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    8. Federica Cugnata & Silvia Salini & Elena Siletti, 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach," IJERPH, MDPI, vol. 18(15), pages 1-10, July.
    9. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    10. Robert Stojnic & Audrey Qiuyan Fu & Boris Adryan, 2012. "A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-13, November.
    11. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
    12. Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.
    13. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    14. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    15. María Morales & Antonio Salmerón & Ana D. Maldonado & Andrés R. Masegosa & Rafael Rumí, 2022. "An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark," Mathematics, MDPI, vol. 10(21), pages 1-21, October.
    16. Nikolaos M. R. Lykoskoufis & Evarist Planet & Halit Ongen & Didier Trono & Emmanouil T. Dermitzakis, 2024. "Transposable elements mediate genetic effects altering the expression of nearby genes in colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    17. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    18. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    19. Scutari Marco & Mackay Ian & Balding David, 2013. "Improving the efficiency of genomic selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 517-527, August.
    20. Mandhani, Jyoti & Nayak, Jogendra Kumar & Parida, Manoranjan, 2020. "Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 320-336.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:dm467. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.