Asymptotics and Random Set Partition

2012 ◽  
pp. 407-450
Keyword(s):  
2020 ◽  
Vol 56 (3) ◽  
pp. 867-891
Author(s):  
Ljuben R. Mutafchiev ◽  
Mladen Savov

10.37236/715 ◽  
2011 ◽  
Vol 18 (1) ◽  
Author(s):  
Anisse Kasraoui

We find exact and asymptotic formulas for the average values of several statistics on set partitions: of Carlitz's $q$-Stirling distributions, of the numbers of crossings in linear and circular representations of set partitions, of the numbers of overlappings and embracings, and of the numbers of occurrences of a 2-pattern.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Carlo Sanna

AbstractFor every positive integer n and for every $$\alpha \in [0, 1]$$ α ∈ [ 0 , 1 ] , let $${\mathcal {B}}(n, \alpha )$$ B ( n , α ) denote the probabilistic model in which a random set $${\mathcal {A}} \subseteq \{1, \ldots , n\}$$ A ⊆ { 1 , … , n } is constructed by picking independently each element of $$\{1, \ldots , n\}$$ { 1 , … , n } with probability $$\alpha $$ α . Cilleruelo, Rué, Šarka, and Zumalacárregui proved an almost sure asymptotic formula for the logarithm of the least common multiple of the elements of $${\mathcal {A}}$$ A .Let q be an indeterminate and let $$[k]_q := 1 + q + q^2 + \cdots + q^{k-1} \in {\mathbb {Z}}[q]$$ [ k ] q : = 1 + q + q 2 + ⋯ + q k - 1 ∈ Z [ q ] be the q-analog of the positive integer k. We determine the expected value and the variance of $$X := \deg {\text {lcm}}\!\big ([{\mathcal {A}}]_q\big )$$ X : = deg lcm ( [ A ] q ) , where $$[{\mathcal {A}}]_q := \big \{[k]_q : k \in {\mathcal {A}}\big \}$$ [ A ] q : = { [ k ] q : k ∈ A } . Then we prove an almost sure asymptotic formula for X, which is a q-analog of the result of Cilleruelo et al.


1979 ◽  
Vol 11 (02) ◽  
pp. 281-282 ◽  
Author(s):  
Noel Cressie
Keyword(s):  

2011 ◽  
Vol 403-408 ◽  
pp. 2341-2344
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
Shou Yu Tong ◽  
De You Fu

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The PF(Particle Filtering) algorithm uses “sequential importance sampling”, previously applied to the posterior of static signals, in which the probability distribution of possible interpretations is represented by a randomly generated set. PF uses learned “sequential Monte Carlo” models, together with practical observations, to propagate and update the random set over time. The result is highly robust tracking of agile motion. Not withstanding the use of stochastic methods, the algorithm runs in near Real-Time.


2012 ◽  
Vol 44 (3) ◽  
pp. 603-616 ◽  
Author(s):  
F. Ballani ◽  
Z. Kabluchko ◽  
M. Schlather

We aim to link random fields and marked point processes, and, therefore, introduce a new class of stochastic processes which are defined on a random set in . Unlike for random fields, the mark covariance function of a random marked set is in general not positive definite. This implies that in many situations the use of simple geostatistical methods appears to be questionable. Surprisingly, for a special class of processes based on Gaussian random fields, we do have positive definiteness for the corresponding mark covariance function and mark correlation function.


2010 ◽  
Vol 27 (1) ◽  
pp. 70-77 ◽  
Author(s):  
Johannes M. Freudenberg ◽  
Siva Sivaganesan ◽  
Mukta Phatak ◽  
Kaustubh Shinde ◽  
Mario Medvedovic

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