Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Bayesian Theory
19941.4k citationsJosé M. Bernardo, A. F. M. Smithprofile →
Reference Posterior Distributions for Bayesian Inference
Countries citing papers authored by José M. Bernardo
Since
Specialization
Citations
This map shows the geographic impact of José M. Bernardo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by José M. Bernardo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José M. Bernardo more than expected).
Fields of papers citing papers by José M. Bernardo
This network shows the impact of papers produced by José M. Bernardo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by José M. Bernardo. The network helps show where José M. Bernardo may publish in the future.
Co-authorship network of co-authors of José M. Bernardo
This figure shows the co-authorship network connecting the top 25 collaborators of José M. Bernardo.
A scholar is included among the top collaborators of José M. Bernardo based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with José M. Bernardo. José M. Bernardo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Berger, James O., José M. Bernardo, & Dongchu Sun. (2015). Overall Objective Priors. Bayesian Analysis. 10(1).69 indexed citations
2.
Berger, James O., José M. Bernardo, & Dongchu Sun. (2012). Objective Priors for Discrete Parameter Spaces. Journal of the American Statistical Association. 107(498). 636–648.29 indexed citations
Bernardo, José M.. (2008). Bayesian Statistics. Palgrave Macmillan eBooks. 1–13.21 indexed citations
5.
Bernardo, José M.. (2007). Objective Bayesian point and region estimation in location-scale models. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 31(1). 3–44.6 indexed citations
6.
Bernardo, José M.. (2007). Bayesian statistics 8 : proceedings of the Eighth Valencia International Meeting, June 2-6, 2006. Oxford University Press eBooks.3 indexed citations
Bernardo, José M.. (2005). Bayesian Reference Analysis.1 indexed citations
10.
Bernardo, José M.. (2002). Una introducció a l'estadística bayesiana. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 17(1). 7–64.1 indexed citations
11.
Bernardo, José M.. (2001). Un programa de síntesis para la enseñanza universitaria de la estadística matemática contemporánea.. 95(1). 87–105.1 indexed citations
12.
Bernardo, José M.. (1999). Model-free objetive Bayesian prediction. 93(3). 295–302.1 indexed citations
Bernardo, José M., et al.. (1988). A Bayesian approach to cluster analysis. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 12(1). 97–112.5 indexed citations
19.
Bernardo, José M.. (1980). Comportamiento asintótico de la información proporcionada por un experimento. Dialnet (Universidad de la Rioja). 625–633.1 indexed citations
20.
Bernardo, José M.. (1979). Expected Information as Expected Utility. The Annals of Statistics. 7(3).379 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.