Mark Huber
Impact in
- Statistics and Probability top 2%
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Inference
- Mathematical Physics top 10%
- Stochastic processes and statistical mechanics
Papers in
-
- Markov Chains and Monte Carlo Methods 24
- Statistical Methods and Inference 8
-
- Bayesian Methods and Mixture Models 11
- Machine Learning and Algorithms 5
- Artificial Intelligence in Games 2
- Co-authors
- Dawn B. Woodard (2 shared papers)Scott C. Schmidler (2 shared papers)James Allen Fill (2 shared papers)Robert L. Wolpert (3 shared papers)David G. Schaeffer (1 shared paper)Brian P. Tighe (1 shared paper)Joshua E. S. Socolar (1 shared paper)Jesper Møller (1 shared paper)
- Journals
- Random Structures and Algorithms (4 papers)Electronic Journal of Probability (3 papers)The Annals of Applied Probability (3 papers)Journal of Applied Probability (2 papers)Advances in Applied Probability (2 papers)
- Partner nations
- United StatesDenmark
In The Last Decade
Mark Huber
34 papers receiving 406 citations
Peers
Comparison fields: 5 of 73
- Statistics and Probability 247
- Mathematical Physics 109
- Artificial Intelligence 169
- Condensed Matter Physics 49
- Computational Mathematics 2
Countries citing papers authored by Mark Huber
This map shows the geographic impact of Mark Huber'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 Mark Huber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Huber more than expected).
Fields of papers citing papers by Mark Huber
This network shows the impact of papers produced by Mark Huber. 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 Mark Huber. The network helps show where Mark Huber may publish in the future.
Co-authors
The 15 scholars most cited alongside Mark Huber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 48 | |
| 2 | 2004 | 43 | |
| 3 | 2005 | 33 | |
| 4 | 2009 | 30 | |
| 5 | 1998 | 28 | |
| 6 | 2010 | 26 | |
| 7 | 2006 | 24 | |
| 8 | 2005 | 22 | |
| 9 | 2010 | 21 | |
| 10 | 2002 | 19 | |
| 11 | 2009 | 16 | |
| 12 | 2015 | 16 | |
| 13 | 2002 | 11 | |
| 14 | Efficient exact sampling from the Ising model using Swendsen-Wang | 1999 | 10 |
| 15 | Fast approximation of the permanent for very dense problems | 2008 | 9 |
| 16 | 2016 | 8 | |
| 17 | 2016 | 8 | |
| 18 | 2009 | 7 | |
| 19 | 2016 | 7 | |
| 20 | 2015 | 6 |
About Mark Huber
Mark Huber is a scholar working on Statistics and Probability, Artificial Intelligence, Mathematical Physics, Applied Mathematics and Condensed Matter Physics, having authored 36 papers that have together received 436 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (24 papers), Stochastic processes and statistical mechanics (13 papers), Bayesian Methods and Mixture Models (11 papers), Statistical Methods and Inference (8 papers), Point processes and geometric inequalities (6 papers), Machine Learning and Algorithms (5 papers), Theoretical and Computational Physics (5 papers) and Artificial Intelligence in Games (2 papers). The work is most often cited by research in Statistics and Probability (247 citations), Mathematical Physics (109 citations), Artificial Intelligence (169 citations), Condensed Matter Physics (49 citations) and Computational Mathematics (2 citations). Mark Huber has collaborated with scholars based in United States and Denmark. Frequent co-authors include Dawn B. Woodard, Scott C. Schmidler, James Allen Fill, Robert L. Wolpert, David G. Schaeffer, Brian P. Tighe, Joshua E. S. Socolar, Jesper Møller, Adrian Dobra and Yuguo Chen. Their work appears in journals such as Random Structures and Algorithms, Electronic Journal of Probability, The Annals of Applied Probability, Journal of Applied Probability and Advances in Applied Probability.
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.