Bayesian image restoration, with two applications in spatial statistics
- Authors
- Julian BesagJeremy York
In The Last Decade
doi.org/10.1007/bf00116466 →Countries where authors are citing Bayesian image restoration, with two applications in spatial statistics
This map shows the geographic impact of Bayesian image restoration, with two applications in spatial statistics. 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 Bayesian image restoration, with two applications in spatial statistics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bayesian image restoration, with two applications in spatial statistics more than expected).
Fields of papers citing Bayesian image restoration, with two applications in spatial statistics
This network shows the impact of Bayesian image restoration, with two applications in spatial statistics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Bayesian image restoration, with two applications in spatial statistics.
About Bayesian image restoration, with two applications in spatial statistics
This paper, published in 1991, received 3.0k indexed citations . Written by Julian Besag and Jeremy York covering the research area of Oncology, Statistics and Probability and Environmental Engineering. It is primarily cited by scholars working on Economics and Econometrics (986 citations), Epidemiology (680 citations), Statistics and Probability (667 citations), Health (449 citations) and General Health Professions (325 citations). Published in Annals of the Institute of Statistical Mathematics.
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.
This paper is also available at doi.org/10.1007/bf00116466.