Chris S. Wallace
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 10%
- Computer Vision and Pattern Recognition
- Signal Processing
- Statistics and Probability top 10%
- Co-authors
- David L. DoweJonathan OliverRohan A. BaxterKevin B. KorbMichael GeorgeffHonghua DaiGopal GuptaKate Walker
- Topics
- Bayesian Modeling and Causal Inference (3 papers)Bayesian Methods and Mixture Models (2 papers)Data Quality and Management (2 papers)
- Journals
- European Journal of PainStatistics and ComputingThe British Journal for the Philosophy of Science
- Partner nations
- AustraliaUnited Kingdom
In The Last Decade
Chris S. Wallace
14 papers receiving 283 citations
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 208
- Computational Theory and Mathematics 55
- Computer Vision and Pattern Recognition 48
- Signal Processing 37
- Statistics and Probability 32
Countries citing papers authored by Chris S. Wallace
This map shows the geographic impact of Chris S. Wallace'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 Chris S. Wallace with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris S. Wallace more than expected).
Fields of papers citing papers by Chris S. Wallace
This network shows the impact of papers produced by Chris S. Wallace. 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 Chris S. Wallace. The network helps show where Chris S. Wallace may publish in the future.
Co-authorship network of co-authors of Chris S. Wallace
This figure shows the co-authorship network connecting the top 25 collaborators of Chris S. Wallace. A scholar is included among the top collaborators of Chris S. Wallace 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 Chris S. Wallace. Chris S. Wallace is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 3 | |
| 3 | 98 | |
| 4 | 17 | |
| 5 | Causal Discovery via MML. | 44 |
| 6 | Unsupervised Learning Using MML. | 86 |
| 7 | A general selection criterion for inductive inference | 38 |
| 8 | Computing Research in Australia. | 1 |
| 9 | Transformed Rejection Generators for Gamma and Normal Pseudo-Random Variables. | 9 |
| 10 | General Linear Multistep Methods To Solve Ordinary Differential Equations. | 18 |
| 11 | A Microprogrammed Lexical Processor. | 4 |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 2 |
About Chris S. Wallace
Chris S. Wallace is a scholar working on Artificial Intelligence, Numerical Analysis and Anesthesiology and Pain Medicine, having authored 14 papers that have together received 327 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Bayesian Methods and Mixture Models (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (208 citations), Statistics and Probability (32 citations) and Computational Theory and Mathematics (55 citations). Chris S. Wallace has collaborated with scholars based in Australia and United Kingdom. Frequent co-authors include David L. Dowe, Jonathan Oliver, Rohan A. Baxter, Kevin B. Korb, Michael Georgeff, Honghua Dai, Gopal Gupta, Kate Walker, Paul White and Jennifer Lewis. Their work appears in journals such as European Journal of Pain, Statistics and Computing and The British Journal for the Philosophy of Science.
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.