An FDA-Based Approach for Clustering Elicited Expert Knowledge

Carlos Barrera-Causil, Juan Carlos Correa, Andrew Zamecnik, Francisco Torres-Avilés*, Fernando Marmolejo-Ramos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

Original languageEnglish
Pages (from-to)184-204
Number of pages21
Issue number1
Publication statusPublished - Mar 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


Dive into the research topics of 'An FDA-Based Approach for Clustering Elicited Expert Knowledge'. Together they form a unique fingerprint.

Cite this