Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl
Preferencje help
Polish version English version Język
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 8

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych:  Markov chain
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Content available remote

Two mutually rarefied renewal processes

100%
Kopocińska I.
Applicationes Mathematicae
|
1993-1995
|
tom 22
|
nr 2
267-273
EN
Let us consider two independent renewal processes generated by appropriate sequences of life times. We say that a renewal time is accepted if in the time between a signal and the preceding one, some signal of the second process occurs. Our purpose is to analyze the sequences of accepted renewals. For simplicity we consider continuous and discrete time separately. In the first case we mainly consider the renewal process rarefied by the Poisson process, in the second we analyze the process generated by the motion of draughtsmen moved by die tossing.
2
Content available remote

Markov chain model of phytoplankton dynamics

80%
Wieczorek R.
International Journal of Applied Mathematics and Computer Science
|
2010
|
tom 20
|
nr 4
763-771
EN
A discrete-time stochastic spatial model of plankton dynamics is given. We focus on aggregative behaviour of plankton cells. Our aim is to show the convergence of a microscopic, stochastic model to a macroscopic one, given by an evolution equation. Some numerical simulations are also presented.
3
Content available remote

Accurate calculations of Stationary Distributions and Mean First Passage Times in Markov Renewal Processes and Markov Chains

80%
Hunter J. J.
Special Matrices
|
2016
|
tom 4
|
nr 1
151-175
EN
This article describes an accurate procedure for computing the mean first passage times of a finite irreducible Markov chain and a Markov renewal process. The method is a refinement to the Kohlas, Zeit fur Oper Res, 30, 197–207, (1986) procedure. The technique is numerically stable in that it doesn’t involve subtractions. Algebraic expressions for the special cases of one, two, three and four states are derived.Aconsequence of the procedure is that the stationary distribution of the embedded Markov chain does not need to be derived in advance but can be found accurately from the derived mean first passage times. MatLab is utilized to carry out the computations, using some test problems from the literature.
4
Content available remote

Mathematical model of mixing in Rumen

80%
Szlenk W.
Applicationes Mathematicae
|
1996-1997
|
tom 24
|
nr 1
87-95
EN
A mathematical model of mixing food in rumen is presented. The model is based on the idea of the Baker Transformation, but exhibits some different phenomena: the transformation does not mix points at all in some parts of the phase space (and under some conditions mixes them strongly in other parts), as observed in ruminant animals.
5
Content available remote

Pairs of successes in Bernoulli trials and a new n-estimator for the binomial distribution

70%
Kühne W., Neumann P., Stoyan D., Stoyan H.
Applicationes Mathematicae
|
1993-1995
|
tom 22
|
nr 3
331-337
EN
The problem of estimating the number, n, of trials, given a sequence of k independent success counts obtained by replicating the n-trial experiment is reconsidered in this paper. In contrast to existing methods it is assumed here that more information than usual is available: not only the numbers of successes are given but also the number of pairs of consecutive successes. This assumption is realistic in a class of problems of spatial statistics. There typically k = 1, in which case the classical estimators cannot be used. The quality of the new estimator is analysed and, for k > 1, compared with that of a classical n-estimator. The theoretical basis for this is the distribution of the number of success pairs in Bernoulli trials, which can be determined by an elementary Markov chain argument.
6
Content available remote

CMPH: a multivariate phase-type aggregate loss distribution

70%
Ren J., Zitikis R.
Dependence Modeling
|
2017
|
tom 5
|
nr 1
304-315
EN
We introduce a compound multivariate distribution designed for modeling insurance losses arising from different risk sources in insurance companies. The distribution is based on a discrete-time Markov Chain and generalizes the multivariate compound negative binomial distribution, which is widely used for modeling insurance losses.We derive fundamental properties of the distribution and discuss computational aspects facilitating calculations of risk measures of the aggregate loss, as well as allocations of the aggregate loss to individual types of risk sources. Explicit formulas for the joint moment generating function and the joint moments of different loss types are derived, and recursive formulas for calculating the joint distributions given. Several special cases of particular interest are analyzed. An illustrative numerical example is provided.
7
Content available remote

Average convergence rate of the first return time

61%
Choe G. H., Kim D. H.
Colloquium Mathematicum
|
2000
|
tom 84/85
|
nr 1
159-171
EN
The convergence rate of the expectation of the logarithm of the first return time $R_{n}$, after being properly normalized, is investigated for ergodic Markov chains. I. Kontoyiannis showed that for any β > 0 we have $log[R_{n}(x)P_{n}(x)] =o(n^{β})$ a.s. for aperiodic cases and A. J. Wyner proved that for any ε >0 we have $-(1 + ε)log n ≤ log[R_{n}(x)P_{n}(x)] ≤ loglog n$ eventually, a.s., where $P_{n}(x)$ is the probability of the initial n-block in x. In this paper we prove that $ E[log R_{(L,S)} - (L-1)h]$ converges to a constant depending only on the process where $R_{(L,S)}$ is the modified first return time with block length L and gap size S. In the last section a formula is proposed for measuring entropy sharply; it may detect periodicity of the process.
8
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Leader election: a Markov chain approach

51%
Grübel R., Hagemann K.
Mathematica Applicanda
|
2016
|
tom 44
|
nr 1
PL
W artykule przywołany jest dobrze znany i szczegółowo zbadany następujący algorytm losowego wyboru lidera. W kolejnych krokach każdy kandydat rzuca monetą. Jeśli wyrzuci orła, to kończy eliminacje (nie przechodzi do następnej tury). Interesuje nas liczba rund do wyłonienia lidera bądź liczba pozostałych kandydatów w powiązaniu z maksimum ciągu zmiennych losowych o rozkładzie geometrycznym. Również wyznaczamy rozkład liczby pozostałych kandydatów jako funkcji liczby tur. W celu odpowiedzi na postawione pytania konstruowane są dwa powiązane ze sobą łańcuch Markowa. Wykorzystując metody teorii potencjału badana jest asymptotyka przy rosnącej początkowej liczbie kandydatów.  Jednym z wykorzystywanych narzędzi jest reprezentacja Renyi-Sukhatme dla statystyk porządkowych rozkładu wykładniczego, która została po raz pierwszy użyta do zagadnienia wyborów lidera przez Brussa i Grubela(2003).
EN
A well-studied randomized election algorithm proceeds as follows: In each round the remaining candidates each toss a coin and leave the competition if they obtain heads. Of interest is the number of rounds required and the number of winners, both related to maxima of geometric random samples, as well as the number of remaining participants as a function of the number of rounds. We introduce two related Markov chains and use ideas and methods from discrete potential theory to analyse the respective asymptotic behaviour as the initial number of participants grows. One of the tools used is the approach via the Rényi-Sukhatme representation of exponential order statistics, which was first used in the leader election context by Bruss and Grübel(2003).
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.