Integer partitions appear in numerous areas of mathematics and its applications — from number theory, algebra and topology to quantum physics, statistics, population genetics, and IT. This classic research topic dates back to Euler, Cauchy, Cayley, Lagrange, Hardy and Ramanujan. The modern statistical approach is to treat partitions as a random ensemble endowed with a suitable probability measure. The uniform (equiprobable) case is well understood but more interesting models (e.g., with certain weights on the components) are mathematically more challenging.
Hirsch [3] introduced his h-index to measure the quality of a researcher’s output, defined as the largest integer n such that the person has h papers with at least h citations each. The h-index has become quite popular (see, e.g., ’Google Scholar’ or ’Web of Science’). Recently, Yong [6] proposed a statistical approach to estimate the h-index using a natural link with the theory of integer partitions [1]. Namely, identifying an integer partition with its Young diagram (with blocks representing parts), it is clear that the h-index is the size of the largest h x h square that fits in.
If partitions of a given integer N are treated as random, with uniform distribution (i.e., all such partitions are assumed to be equally likely), then their Young diagrams have "limit shape" (under the suitable scaling), first identified by Vershik [5]. Yong’s idea is to use the limit shape to deduce certain statistical properties of the h-index. In particular, it follows that the "typical" value of Hirsch’s index for someone with a large number N of citations should be close to 0.54 N.
However, the assumption of uniform distribution on partitions is of course rather arbitrary, and needs to be tested statistically. This issue is important since the limit shape may strongly depend on the distribution of partitions [2], which would also affect the asymptotics of Hirsch’s index. Thus, the idea of this project is to explore such an extension of Yong’s approach. To this end, one might try and apply Markov chain Monte Carlo (MCMC) techniques [4], whereby the uniform distribution may serve as an "uninformed prior".
These and similar ideas have a potential to be extended beyond the citation topic, and may offer an interesting blend of theoretical and more applied issues, with a possible gateway to further applications of discrete probability and statistics in social sciences.
Successful candidates should have a good degree in mathematics and/or statistics. Programming skills to carry out MCMC simulations would be useful but not essential, as the appropriate training will be provided..
You will be based within a strong research group in Probability, Stochastic Modelling & Financial Mathematics (
http://www.maths.leeds.ac.uk/research/groups/probability-stochastic-modelling-and-financial-mathematics.html). Our research focusses on the study and modelling of systems and processes featured by uncertainty and/or complexity, using advanced theoretical, simulation and numerical methods. It covers a vast variety of modern topics both in probability (including theory of random processes and stochastic analysis) and in a wide range of applications in mathematical and other sciences, spanning from nonlinear dynamical systems and mathematical physics through mathematical biology and complexity theory to mathematical finance and economics.
Informal enquiries: Dr Leonid Bogachev (
[email protected]).
References
[1] Andrews, G.E. and Eriksson, K. Integer Partitions. Cambridge Univ. Press, Cambridge, 2004.
[2] Bogachev, L.V. Unified derivation of the limit shape for multiplicative ensembles of random integer partitions with equiweighted parts. Random Struct. Algorithms, 47 (2015), 227–266. (doi:10.1002/rsa.20540)
[3] Hirsch, J.E. An index to quantify an individual's scientific research output. Proc. Natl. Acad. Sci. USA, 102 (2005), 16569–16572. (doi:10.1073/pnas.0507655102)
[4] Markov Chain Monte Carlo in Practice (W.R. Gilks, S. Richardson and D.J. Spiegelhalter, eds.). Chapman & Hall/CRC, London, 1996.
[5] Vershik, A.M. Asymptotic combinatorics and algebraic analysis. In: Proc. Intern. Congress Math. 1994, vol. 2. Birkhäuser, Basel, 1995, pp. 1384–1394. (www.mathunion.org/ICM/ICM1994.2/Main/icm1994.2.1384.1394.ocr.pdf)
[6] Yong, A. Critique of Hirsch's citation index: a combinatorial Fermi problem. Notices Amer. Math. Soc., 61 (2014), 1040–1050. (doi:/10.1090/noti1164)