Radford M. Neal Explained
Radford M. Neal is a professor emeritus at the Department of Statistics and Department of Computer Science at the University of Toronto, where he holds a research chair in statistics and machine learning.
Education and career
Neal studied computer science at the University of Calgary, where he received his B.Sc. in 1977 and M.Sc. in 1980, with thesis work supervised by David Hill. He worked for several years as a sessional instructor at the University of Calgary and as a statistical consultant in the industry before coming back to the academia. Neal continued his study at the University of Toronto, where he received his Ph.D. in 1995 under the supervision of Geoffrey Hinton.[2] Neal became an assistant professor at the University of Toronto in 1995, an associated professor in 1999 and a full professor since 2001. He was the Canada Research Chair in Statistics and Machine Learning from 2003 to 2016 and retired in 2017.
Neal has made great contributions in the area of machine learning and statistics, where he is particularly well known for his work on Markov chain Monte Carlo,[3] [4] error correcting codes[5] and Bayesian learning for neural networks.[6] He is also known for his blog[7] and as the developer of pqR: a new version of the R interpreter.[8]
Bibliography
Books and chapters
- Book: Neal, Radford M. . Bayesian learning for neural networks . 1996 . Springer . 0-387-94724-8 . New York . 34894370.
- Book: Neal, Radford M. . Steve . Andrew . Galin . Xiao-Li . Brooks . Gelman . Jones . Meng . 2011-05-10 . MCMC using Hamiltonian dynamics . 10.1201/b10905. 1206.1901 . 2011hmcm.book..113N . 9780429138508 . 1048042 .
Selected papers
- Witten . Ian H. . Neal . Radford M. . Cleary . John G. . 1987 . Arithmetic coding for data compression . Communications of the ACM . en . 30 . 6 . 520–540 . 10.1145/214762.214771 . 3343393 . 0001-0782. free .
- Hinton . Geoffrey E. . Dayan . Peter . Frey . Brendan J. . Neal . Radford M. . 1995-05-26 . The "Wake-Sleep" Algorithm for Unsupervised Neural Networks . Science . en . 268 . 5214 . 1158–1161 . 10.1126/science.7761831 . 7761831 . 1995Sci...268.1158H . 871473 . 0036-8075.
- Dayan . Peter . Hinton . Geoffrey E. . Neal . Radford M. . Zemel . Richard S. . 1995 . The Helmholtz Machine . Neural Computation . en . 7 . 5 . 889–904 . 10.1162/neco.1995.7.5.889 . 7584891 . 1890561 . 0899-7667.
- Neal . Radford M. . 2000 . Markov Chain Sampling Methods for Dirichlet Process Mixture Models . Journal of Computational and Graphical Statistics . 9 . 2 . 249–265 . 10.2307/1390653 . 1390653 . 1061-8600.
- Neal . Radford M. . 2001 . Annealed importance sampling . Statistics and Computing . 11 . 2 . 125–139 . 10.1023/A:1008923215028. 11112994 .
- Neal . Radford M. . 2003-06-01 . Slice sampling . The Annals of Statistics . 31 . 3 . 10.1214/aos/1056562461 . 0090-5364. free .
- Jain . Sonia . Neal . Radford M. . 2007-09-01 . Splitting and merging components of a nonconjugate Dirichlet process mixture model . Bayesian Analysis . 2 . 3 . 10.1214/07-BA219 . 1936-0975. free .
- Shahbaba . Babak . Lan . Shiwei . Johnson . Wesley O. . Neal . Radford M. . 2014 . Split Hamiltonian Monte Carlo . Statistics and Computing . en . 24 . 3 . 339–349 . 10.1007/s11222-012-9373-1 . 255067283 . 0960-3174. 1106.5941 .
Notes and References
- Web site: Radford M. Neal Curriculum Vitae . User radford at cs.utoronto.ca . 4 May 2015.
- Web site: Neal . Radford M. . 2022-05-31 . Curriculum Vitae .
- Neal . Radford . Probabilistic Inference Using Markov Chain Monte Carlo Methods . 144 . Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto . 1993 . 9 May 2015.
- Book: Neal, Radford M . Handbook of Markov Chain Monte Carlo. 2011 . Chapman and Hall/CRC . 978-0470177938 . Steve Brooks . Andrew Gelman . Galin L. Jones . Xiao-Li Meng. MCMC Using Hamiltonian Dynamics . http://www.mcmchandbook.net/HandbookChapter5.pdf.
- MacKay . D. J. C. . David J. C. MacKay. Neal . R. M. . 10.1049/el:19961141 . Near Shannon limit performance of low density parity check codes . Electronics Letters . 32 . 18 . 1645 . 1996 . 1996ElL....32.1645M .
- Book: 10.1007/978-1-4612-0745-0. Bayesian Learning for Neural Networks. 118. Lecture Notes in Statistics. 1996. Neal . R. M. . 978-0-387-94724-2.
- Web site: Radford Neal's blog . 9 May 2015.
- Web site: pqR - a pretty quick version of R . 9 May 2015.