RM Neal: h-index, Total Citations, and Citation Map
RM Neal's h-index is 53 (89 i10-index, 56,931+ total citations across 132+ publications) according to Google Scholar as of September 2026. RM Neal is affiliated with U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!.
RM Neal is a researcher affiliated with U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!, specializing in various fields. Their work has been cited 56,931 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
RM Neal's Citation Metrics
Bibliometric impact based on 132 indexed publications.
- H-Index
- 53
- i10-Index
- 89
- Total Citations
- 56,931
- Citing Countries
- 51
As of September 2026.
RM Neal has an h-index of 53 and 56,931 total citations across 132 publications, with research cited by institutions in 51 countries.
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Global Impact Map
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Bayesian learning for neural networks
19958,340
Top Citing Countries
Top Citing Institutions
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Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
15 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered Bayesian learning for neural networks, establishing a foundational framework for probabilistic inference in deep learning that has been widely adopted and extended by the independent research community.
The researcher developed foundational methods for sampling from complex distributions, establishing a lineage from tempered transitions to Hamiltonian dynamics that significantly advanced Markov chain Monte Carlo techniques.
The researcher established a foundational framework for Bayesian learning through stochastic dynamics, subsequently advancing probabilistic inference via Markov chain Monte Carlo methods.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About RM Neal's research
RM Neal is a researcher at U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!. Their work has been cited 56,931 times across 132 publications (h-index 53), according to Google Scholar.
Their most-cited work, “Bayesian learning for neural networks” (1995), has accumulated 8,340 citations. Other influential works include “MCMC Using Hamiltonian Dynamics” (2011) with 5,609 citations and “Near Shannon limit performance of low density parity check codes” (1997) with 5,344 citations.
Citations of RM Neal's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
