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Tom Liggett, some brief reflections
www.statslab.cam.ac.uk/~grg/papers/tml-proof.pdf3 Sep 2020: 17. 18. 19. 20201/2. 21. 22. 23. 24. 25. 26. 27. ... 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. -
Discussion of Random Projection Ensemble Classificationby Timothy I.…
www.statslab.cam.ac.uk/~rds37/papers/CHEN_SHAH.pdf2 Jan 2020: References. Breiman, L. (1996). Stacked regressions. Machine Learning, 24, 49–64. Wolpert, D. -
IB Optimisation: Lecture 1
www.statslab.cam.ac.uk/~mike/optimisation/lecture1.pdf24 Apr 2020: IB Optimisation: Lecture 1. Mike Tehranchi. University of Cambridge. 24 April 2020. -
CORRECTIONS for ‘Mathematical foundations of infinite-dimensional…
www.statslab.cam.ac.uk/~nickl/Site/__files/CORRECTIONS.pdf19 Dec 2020: p.24, in equation (2.3) and the line after it, replace (twice) ‘1/π2’ by ‘4/π2’, and in line -1, replaceonce more ‘K = 1π2 ’ by ‘K =. 4π2 ’. p.25, ... 2.21), replace ‘t2’ by ‘u2’. p.37, equation (2.24): replace ‘Eeξ2/c2 =. -
Mixing times of Markov chains Perla Sousi∗ December 8, ...
www.statslab.cam.ac.uk/~ps422/mixing-notes.pdf8 Dec 2020: 16. 3.2 Examples. 20. 3.3 Hitting time bound. 23. 4 Dirichlet form and the bottleneck ratio 24. ... 24. Corollary 4.2. Let P be a reversible matrix with respect to π. -
Random Planar Geometry
www.statslab.cam.ac.uk/~jpm205/teaching/lent2020/rpg_notes.pdf11 Mar 2020: 6. Planar maps 12. 7. Random planar maps 19. 8. Conformal mapping review 24. ... 24 JASON MILLER. (3) In general, one has the “same behavior” for planar maps chosen uniformly at random from. -
Math. Stat. Learn. 2 (2019), 165–216DOI 10.4171/MSL/14 Mathematical…
www.statslab.cam.ac.uk/~nickl/Site/__files/MSL.pdf24 Sep 2020: In the context of EIT we refer to the articles [10,12,21,22,24,42] andthe many references therein. ... JKU DXjJ. XkK. hU.r/j ;. 0/. kiL2.@D/b. r/. jk: (24). Lemma 4. -
Abstract We survey the published work of Harry Kesten ...
www.statslab.cam.ac.uk/~grg/papers/kesten-ptrf-final.pdf13 Nov 2020: At the time of Harry’s move from theNetherlands to the USA in 1956, as a graduate student aged 24, much of thefoundational infrastructure of probability was in place. -
Mathematical Foundations of Infinite-Dimensional Statistical Models
www.statslab.cam.ac.uk/~nickl/Site/__files/FULLPDF.pdf25 Feb 2020: Mathematical Foundations of Infinite-DimensionalStatistical Models. In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood and Bayesianposterior inference does
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