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This paper introduces a split-and-merge transformation of interval partitions which combines some features of one model studied by Gnedin and Kerov [12, 11] and another studied by Tsilevich [30, 31] and Mayer-Wolf, Zeitouni and Zerner [21]. The invariance under this split-and-merge transformation of the interval partition generated by a suitable Poisson process yields a simple proof of the recent result of [21] that a Poisson–Dirichlet distribution is invariant for a closely related fragmentation–coagulation process. Uniqueness and convergence to the invariant measure are established for the split-and-merge transformation of interval partitions, but the corresponding problems for the fragmentation–coagulation process remain open.
We present improved lower and upper bounds for the time constant of first-passage percolation on the square lattice. For the case of lower bounds, a new method, using the idea of a transition matrix, has been used. Numerical results for the exponential and uniform distributions are presented. A simulation study is included, which results in new estimates and improved upper confidence limits for the time constants.
For two stochastically dependent random variables X and Y taking values in {0,…, m−1}, we study the distribution of the random residue U = XY mod m. Our main result is an upper bound for the distance Δm = supx∈[0,1] [mid ] P(U/m [les ] x)−x[mid ]. For independent and uniformly distributed X and Y, the exact distribution of U is derived and shown to be stochastically smaller than the uniform distribution on {0,…, m−1}. Moreover, in this case Δm is given explicitly.
Let q be an integer with q [ges ] 2. We give a new proof of a result of Erdös and Turán determining the proportion of elements of the finite symmetric group Sn having no cycle of length a multiple of q. We then extend our methods to the more difficult case of obtaining the proportion of such elements in the finite alternating group An. In both cases, we derive an asymptotic formula with error term for the above mentioned proportion, which contains an unexpected occurrence of the Gamma-function.
We apply these results to estimate the proportion of elements of order 2f in Sn, and of order 3f in An and Sn, where gcd(2, f) = 1, and gcd(3, f) = 1, respectively, and log f is polylogarithmic in n. We also give estimates for the probability that the fth power of such elements is a transposition or a 3-cycle, respectively. An algorithmic application of these results to computing in An or Sn, given as a black-box group with an order oracle, is discussed.
We consider k-uniform set systems over a universe of size n such that the size of each pairwise intersection of sets lies in one of s residue classes mod q, but k does not lie in any of these s classes. A celebrated theorem of Frankl and Wilson [8] states that any such set system has size at most (ns) when q is prime. In a remarkable recent paper, Grolmusz [9] constructed set systems of superpolynomial size Ω(exp(c log2n/log log n)) when q = 6. We give a new, simpler construction achieving a slightly improved bound. Our construction combines a technique of Frankl [6] of ‘applying polynomials to set systems’ with Grolmusz's idea of employing polynomials introduced by Barrington, Beigel and Rudich [5]. We also extend Frankl's original argument to arbitrary prime-power moduli: for any ε > 0, we construct systems of size ns+g(s), where g(s) = Ω(s1−ε). Our work overlaps with a very recent technical report by Grolmusz [10].
Let G be a connected graph that is 2-cell embedded in a surface S, and let G* be its topological dual graph. We will define and discuss several matroids whose element set is E(G), for S homeomorphic to the plane, projective plane, or torus. We will also state and prove old and new results of the type that the dual matroid of G is the matroid of the topological dual G*.
Let {Av}v∈V be a finite collection of events and G = (V, E) be a chordal graph. Our main result – the chordal graph sieve – is a Bonferroni-type inequality where the selection of intersections in the estimates is determined by a chordal graph G. It interpolates between Boole's inequality (G empty) and the sieve formula (G complete). By varying G, several inequalities both well-known and new are obtained in a concise and unified way.
We show that the limiting distribution of the number of comparisons used by Hoare's quickselect algorithm when given a random permutation of n elements for finding the mth-smallest element, where m = o(n), is the Dickman function. The limiting distribution of the number of exchanges is also derived.
We show that, if G is a graph of order n with maximal degree Δ(G) and minimal degree δ(G) whose complement contains no K2,s, s [ges ] 2, then G contains every tree T of order n−s+1 whose maximal degree is at most Δ(G) and whose vertex of second-largest degree is at most δ(G). We then show that this result implies that special cases of two conjectures are true. We verify that the Erdös–Sós conjecture, which states that a graph whose average degree is larger than k−1 contains every tree of order k+1, is true for graphs whose complement does not contain a K2,4, and the Komlós–Sós conjecture, which states that every graph of median degree at least k contains every tree of order k+1, is true for graphs whose complement does not contain a K2,3.
Consider a finite alphabet Ω and patterns which consist of characters from Ω. For a given pattern w, let cor(w) denote its autocorrelation, which can be seen as a measure of the amount of overlap in w. Letting aw(n) denote the number of strings over Ω of length n which do not contain w as a substring, the main result of this paper is: If cor(w) > cor(w′) then aw(n)−aw′(n) > (|Ω|−1)(aw(n−1)−aw′(n−1)) for n [ges ] N, and the value of N is given. This result confirms a conjecture by Eriksson [2], which was previously proved to be true by Cakir, Chryssaphinou and Månsson [1] when |Ω| [ges ] 3.
Consider the class of graphs on n vertices which have maximum degree at most 1/2n−1+τ, where τ [ges ] −n1/2+ε for sufficiently small ε > 0. We find an asymptotic formula for the number of such graphs and show that their number of edges has a normal distribution whose parameters we determine. We also show that expectations of random variables on the degree sequences of such graphs can often be estimated using a model based on truncated binomial distributions.
Let r = r(n) → ∞ with 3 [les ] r [les ] n1−η for an arbitrarily small constant η > 0, and let Gr denote a graph chosen uniformly at random from the set of r-regular graphs with vertex set {1, 2, …, n}. We prove that, with probability tending to 1 as n → ∞, Gr has the following properties: the independence number of Gr is asymptotically 2n log r/r and the chromatic number of Gr is asymptotically r/2nlogr.
We study the complexity of computing the coefficients of three classical polynomials, namely the chromatic, flow and reliability polynomials of a graph. Each of these is a specialization of the Tutte polynomial Σtijxiyj. It is shown that, unless NP = RP, many of the relevant coefficients do not even have good randomized approximation schemes. We consider the quasi-order induced by approximation reducibility and highlight the pivotal position of the coefficient t10 = t01, otherwise known as the beta invariant.
Our nonapproximability results are obtained by showing that various decision problems based on the coefficients are NP-hard. A study of such predicates shows a significant difference between the case of graphs, where, by Robertson–Seymour theory, they are computable in polynomial time, and the case of matrices over finite fields, where they are shown to be NP-hard.
“It is curious how often the most acute and powerful intellects have gone astray in the calculation of probabilities.”
William Stanley Jevons
THE MEANING OF PROBABILITY
The single term ‘probability’ can be used in several distinct senses. These fall into two main groups. A probability can be a limiting ratio in a sequence of repeatable events. Thus the statement that a coin has a 50% probability of landing heads is usually taken to mean that approximately half of a series of tosses will be heads, the ratio becoming ever more exact as the series is extended. But a probability can also stand for something less tangible: a degree of knowledge or belief. In this case, the probability can apply not just to sequences, but also to single events. The weather forecaster who predicts rain tomorrow with a probability of ½ is not referring to a sequence of future days. He is concerned more to make a reliable forecast for tomorrow than to speculate further ahead; besides, the forecast is based on particular atmospheric conditions that will never be repeated. Instead, the forecaster is expressing his confidence of rain, based on all the available information, as a value on a scale on which 0 and 1 represent absolute certainty of no rain and rain respectively.
The former is called the frequency interpretation of probability, the latter the epistemic or ‘degree of belief’ or Bayesian interpretation, after the Reverend Thomas Bayes, an eighteenth-century writer on probability.
Ronald Aylmer Fisher was born in 1890 in East Finchley, London. An academic prodigy at Harrow, his interests ranged from mathematics and astronomy to biology and evolution. His mother died while he was still at school, and shortly afterwards his father, an auctioneer of fine art, bankrupted himself through a series of disastrous business deals. The once-prosperous family fell on hard times. In 1908, however, Fisher won a scholarship to Cambridge. Though the rote nature of the degree course in biology led him to plump for mathematics instead, the young Fisher continued to read widely, and finishing the three years of mathematics, returned to Cambridge in 1912 on a one-year physics scholarship.
In this final year, Fisher attended lectures by James Jeans on the quantum theory, and by his college tutor, the astronomer F.J.M. Stratton, on the theory of errors. But by this stage he had discovered a perfect combination of his mathematical and biological interests in Karl Pearson's “mathematical contributions to the theory of evolution.” Fisher was especially attracted to Pearson's focus on human hereditary, and took to eugenics with idealistic zeal. With a group of like-minded friends, he was instrumental in establishing a Cambridge University Eugenics Society – John Maynard Keynes was another founding member – and led discussions of Pearson's ideas and their implications for society. Fisher acted as a steward at the First International Eugenics Conference in 1912, and addressed the larger London Society in October of the following year.
The calculus of probability is conventionally dated from July 1654, when Blaise Pascal wrote Pierre de Fermat with a question raised by a friend, the Chevalier de Méré, concerning a dice game. The subsequent correspondence, ranging widely over gambling problems, was not the first time that games of chance had been addressed mathematically. Muslim and Jewish mathematicians in the twelfth and thirteenth centuries had calculated combinatorial rules, and Renaissance scholars in the fifteenth and sixteenth centuries had analyzed card games and dice throws in terms of the number of ways of reaching each possible outcome. Yet the two savants were the first to treat the subject in a consistent and unified way. The ‘problem of points’ posed by the Chevalier – concerning the division of stakes between players when a game is interrupted before reaching conclusion – had resisted attempts at solution from the fourteenth century. Pascal and Fermat gave the first correct general solution to this and other problems, and developed new mathematical techniques to calculate the odds in a number of card and dice games.
Following Pascal and Fermat but working largely independently, Christian Huygens derived similar results in his 1657 essay, De Ratiociniis in Ludo Aleae. Pierre Rémond de Montmort calculated the expected gains in several complex card and dice games in a publication of 1708.