Methodology And Computing In Applied Probability

1.1k papers and 8.6k indexed citations
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About

The 1.1k papers published in Methodology And Computing In Applied Probability in the last decades have received a total of 8.6k indexed citations. Papers published in Methodology And Computing In Applied Probability usually cover Statistics and Probability (402 papers), Finance (327 papers) and Management Science and Operations Research (325 papers) specifically the topics of Probability and Risk Models (235 papers), Stochastic processes and financial applications (210 papers) and Statistical Distribution Estimation and Applications (201 papers). The most active scholars publishing in Methodology And Computing In Applied Probability are Reuven Y. Rubinstein, Gareth O. Roberts, Dirk P. Kroese, Osnat Stramer, Yves F. Atchadé, Markos V. Koutras, Jakob Gulddahl Rasmussen, Leyuan Shi, Kaiyong Wang and Yuebao Wang.

In The Last Decade

Fields of papers published in Methodology And Computing In Applied Probability

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Methodology And Computing In Applied Probability. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Methodology And Computing In Applied Probability.

Countries where authors publish in Methodology And Computing In Applied Probability

Since Specialization
Citations

This map shows the geographic impact of research published in Methodology And Computing In Applied Probability. 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 papers published in Methodology And Computing In Applied Probability with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Methodology And Computing In Applied Probability more than expected).

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.

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