P
Paolo Giudici
Researcher at University of Pavia
Publications - 223
Citations - 4542
Paolo Giudici is an academic researcher from University of Pavia. The author has contributed to research in topics: Systemic risk & Credit risk. The author has an hindex of 28, co-authored 198 publications receiving 3488 citations. Previous affiliations of Paolo Giudici include University of Cambridge & Marche Polytechnic University.
Papers
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Book
Applied Data Mining: Statistical Methods for Business and Industry
TL;DR: This paper presents a meta-analysis of data mining methods used in the management of business cases and some of the techniques used in this analysis were previously described in the preface.
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Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions
TL;DR: In this paper, the authors consider reversible jump Markov chain Monte Carlo methods and propose a Taylor series expansion of the acceptance probability around certain canonical jumps to guide the choice of proposal.
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Decomposable graphical Gaussian model determination
Paolo Giudici,Peter H.R. Green +1 more
TL;DR: A hyper inverse Wishart prior distribution on the concentration matrix for each given graph is considered, containing only the elements for which the corresponding element of the inverse is nonzero, allowing all computations to be performed locally, at the clique level, which is a clear advantage for the analysis of large and complex datasets.
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Improving Markov Chain Monte Carlo model search for data mining
Paolo Giudici,Robert Castelo +1 more
TL;DR: The motivation of this paper is the application of MCMC model scoring procedures to data mining problems, involving a large number of competing models and other relevant model choice aspects, and the proposed MC3 algorithm, which provides an efficient general framework for computations with both Directed Acyclic Graphical (DAG) models and Undirected Decomposable Models (UDG).
Book
Applied Data Mining for Business and Industry
Paolo Giudici,Silvia Figini +1 more
TL;DR: A large number of the models used in this study are logistic regression-based, which is a very simple way of looking at the structure of the data and its role in the design of the model.