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Bayesian data mining, with application to benchmarking and credit scoring

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  • Paolo Giudici

Abstract

The purpose of this article is to show that Bayesian methods, coupled with Markov chain Monte Carlo computational techniques, can be successfully employed in the analysis of highly dimensional complex datasets, such as those occurring in data mining applications. Our methodology employs conditional independence graphs to localize model specification and inferences, thus allowing a considerable gain in flexibility of modelling and efficiency of the computations. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Paolo Giudici, 2001. "Bayesian data mining, with application to benchmarking and credit scoring," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 69-81, January.
    • Handle: RePEc:wly:apsmbi:v:17:y:2001:i:1:p:69-81
    DOI: 10.1002/asmb.425
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    Cited by:

    1. Paola Cerchiello & Paolo Giudici, 2013. "Bayesian Credit Ratings (new version)," DEM Working Papers Series 030, University of Pavia, Department of Economics and Management.
    2. Dan Cheng & Pasquale Cirillo, 2019. "An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages," Risks, MDPI, vol. 7(3), pages 1-21, July.
    3. Ning Fu & Mingu Kang & Joongi Hong & Suntae Kim, 2024. "Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets," Mathematics, MDPI, vol. 12(5), pages 1-21, March.
    4. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
    5. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    6. Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    7. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    8. Paola Cerchiello & Paolo Giudici, 2012. "Bayesian Credit Rating Assessment," DEM Working Papers Series 019, University of Pavia, Department of Economics and Management.

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