Galin L. Jones

5.7k total citations · 1 hit paper
58 papers, 3.3k citations indexed

About

Galin L. Jones is a scholar working on Statistics and Probability, Artificial Intelligence and Small Animals. According to data from OpenAlex, Galin L. Jones has authored 58 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Statistics and Probability, 21 papers in Artificial Intelligence and 6 papers in Small Animals. Recurrent topics in Galin L. Jones's work include Markov Chains and Monte Carlo Methods (26 papers), Statistical Methods and Inference (24 papers) and Bayesian Methods and Mixture Models (19 papers). Galin L. Jones is often cited by papers focused on Markov Chains and Monte Carlo Methods (26 papers), Statistical Methods and Inference (24 papers) and Bayesian Methods and Mixture Models (19 papers). Galin L. Jones collaborates with scholars based in United States, United Kingdom and Canada. Galin L. Jones's co-authors include Xiao‐Li Meng, Andrew Gelman, Steve Brooks, Brian Caffo, James P. Hobert, James M. Flegal, Ronald C. Neath, Murali Haran, Richard C. Hill and Wolfgang Jank and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Journal of Nutrition.

In The Last Decade

Galin L. Jones

56 papers receiving 3.2k citations

Hit Papers

Handbook of Markov Chain Monte Carlo 2011 2026 2016 2021 2011 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Galin L. Jones United States 19 1.1k 933 255 198 182 58 3.3k
Ajay Jasra United Kingdom 23 1.4k 1.2× 1.8k 1.9× 386 1.5× 195 1.0× 94 0.5× 113 3.2k
Antonietta Mira Switzerland 18 642 0.6× 650 0.7× 340 1.3× 164 0.8× 144 0.8× 97 2.6k
S. Rao Jammalamadaka United States 22 1.1k 0.9× 674 0.7× 235 0.9× 111 0.6× 112 0.6× 118 2.7k
Scott A. Sisson Australia 29 938 0.8× 870 0.9× 152 0.6× 208 1.1× 204 1.1× 107 3.8k
Omiros Papaspiliopoulos United Kingdom 20 794 0.7× 929 1.0× 131 0.5× 104 0.5× 118 0.6× 40 1.8k
Steve Brooks United Kingdom 9 553 0.5× 525 0.6× 194 0.8× 173 0.9× 122 0.7× 13 2.3k
Heikki Haario Finland 31 741 0.6× 848 0.9× 576 2.3× 399 2.0× 153 0.8× 161 5.8k
Andrew L. Rukhin United States 21 889 0.8× 617 0.7× 410 1.6× 102 0.5× 185 1.0× 160 2.6k
John T. Kent United Kingdom 29 1.1k 1.0× 870 0.9× 165 0.6× 450 2.3× 262 1.4× 92 3.6k
Eero Saksman Finland 19 779 0.7× 689 0.7× 503 2.0× 207 1.0× 142 0.8× 71 4.1k

Countries citing papers authored by Galin L. Jones

Since Specialization
Citations

This map shows the geographic impact of Galin L. Jones'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 Galin L. Jones with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Galin L. Jones more than expected).

Fields of papers citing papers by Galin L. Jones

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Galin L. Jones. 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 Galin L. Jones. The network helps show where Galin L. Jones may publish in the future.

Co-authorship network of co-authors of Galin L. Jones

This figure shows the co-authorship network connecting the top 25 collaborators of Galin L. Jones. A scholar is included among the top collaborators of Galin L. Jones 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 Galin L. Jones. Galin L. Jones 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
2.
Pagnottoni, Paolo, et al.. (2024). Bayesian variable selection for matrix autoregressive models. Statistics and Computing. 34(2). 5 indexed citations
3.
Criswell, A. W., Andreas Bauswein, Katerina Chatziioannou, et al.. (2023). Hierarchical Bayesian method for constraining the neutron star equation of state with an ensemble of binary neutron star postmerger remnants. Physical review. D. 107(4). 10 indexed citations
4.
Jones, Galin L., et al.. (2023). Exact convergence analysis for metropolis–hastings independence samplers in Wasserstein distances. Journal of Applied Probability. 61(1). 33–54. 4 indexed citations
5.
Flegal, James M., et al.. (2019). New visualizations for Monte Carlo simulations. arXiv (Cornell University). 1 indexed citations
6.
Jones, Galin L., et al.. (2017). Multivariate initial sequence estimators in Markov chain Monte Carlo. Journal of Multivariate Analysis. 159. 184–199. 21 indexed citations
7.
Johnson, Alicia A. & Galin L. Jones. (2015). Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models. Journal of Multivariate Analysis. 140. 325–342. 7 indexed citations
8.
Neath, Ronald C. & Galin L. Jones. (2009). Variable-at-a-time Implementations of Metropolis-Hastings. arXiv (Cornell University). 3 indexed citations
9.
Johnson, Alicia A., Galin L. Jones, & Ronald C. Neath. (2009). Component-wise Markov chain Monte Carlo. Statistical Science. 28. 4 indexed citations
10.
Cooke, Kirsten L., et al.. (2009). Effect of antivenin dose on outcome from crotalid envenomation: 218 dogs (1988–2006). Journal of Veterinary Emergency and Critical Care. 19(6). 603–610. 26 indexed citations
11.
Johnson, Alicia A. & Galin L. Jones. (2007). Gibbs Sampling for a Bayesian Hierarchical Version of the General Linear Mixed Model. arXiv (Cornell University). 6 indexed citations
12.
Hill, Richard C., Colin F. Burrows, John E. Bauer, et al.. (2006). Texturized Vegetable Protein Containing Indigestible Soy Carbohydrate Affects Blood Insulin Concentrations in Dogs Fed High Fat Diets. Journal of Nutrition. 136(7). 2024S–2027S. 8 indexed citations
13.
Caffo, Brian, Wolfgang Jank, & Galin L. Jones. (2005). Ascent-Based Monte Carlo Expectation– Maximization. Journal of the Royal Statistical Society Series B (Statistical Methodology). 67(2). 235–251. 78 indexed citations
14.
Caffo, Brian, et al.. (2003). Ascent-Based Monte Carlo EM. 67. 18 indexed citations
15.
Andrew, Stacy E., An Nguyen, Galin L. Jones, & Dennis E. Brooks. (2003). Seasonal effects on the aerobic bacterial and fungal conjunctival flora of normal thoroughbred brood mares in Florida. Veterinary Ophthalmology. 6(1). 45–50. 82 indexed citations
16.
Scott, Karen C., et al.. (2002). Supplemental Vitamin C Appears to Slow Racing Greyhounds. Journal of Nutrition. 132(6). 1616S–1621S. 46 indexed citations
17.
Scott, Karen C., et al.. (2002). Serum ascorbic acid concentrations in previously unsupplemented greyhounds after administration of a single dose of ascorbic acid intravenously or per os. Journal of Animal Physiology and Animal Nutrition. 86(7-8). 222–228. 6 indexed citations
18.
Walker, Mark, Richard C. Hill, W. Grant Guilford, et al.. (2001). Postprandial Venous Ammonia Concentrations in the Diagnosis of Hepatobiliary Disease in Dogs. Journal of Veterinary Internal Medicine. 15(5). 463–466. 19 indexed citations
19.
Hill, Richard C., Daniel D. Lewis, Karen C. Scott, et al.. (2001). Effect of increased dietary protein and decreased dietary carbohydrate on performance and body composition in racing Greyhounds. American Journal of Veterinary Research. 62(3). 440–447. 20 indexed citations
20.
Hill, Richard C., et al.. (2000). Maintenance energy requirements and the effect of diet on performance of racing Greyhounds. American Journal of Veterinary Research. 61(12). 1566–1573. 23 indexed citations

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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