Results 11 to 20 of about 2,778 (137)
Une définition générale de l'influence entre processus stochastiques [PDF]
, 2009 Nous présentons la notion d'indépendance locale faible conditionnelle (WCLI) entre processus stochastiques dans une large classe de semi-martingales, que nous appelons $\DMdet'$. La définition est fondée sur la mesurabilité des caractéristiques de la décomposition de la semi-martingale cible.
Commenges, Daniel, Gégout-Petit, Anne
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Etude de processus stochastiques non linéaires
, 1997 Our study deals with processes which are solutions of stochastic differentialequations in which the law of the solution can interact . We etablish an instabilityphenomena for a two-dimensional stochastic process which density verifies a P.D.E.of Burgers' type: the solution fluctuates when the diffusion tends to zero.
Wantz Mézières, Sophie
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Comportement en temps long de différent processus stochastiques en neuroscience [PDF]
, 2022 In this thesis, we focus on two stochastic models which can be applied to neuroscience : Hawkes model and FitzHugh-Nagumo model. We study their long-time behavior. The first chapter deals with cumulative processes, which are a larger processes class than
Colombani, Laetitia
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Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]
, 2009 Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D. +3 more
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What random variable generates a bounded potential?
International Journal of Stochastic Analysis, Volume 2004, Issue 1, Page 97-106, 2004., 2004 It is known that if a predictable nondecreasing process generates a bounded potential, then its final value satisfies the Garsia inequality. We prove the converse, that is, a random variable satisfying the Garsia inequality generates a bounded potential.
N. Kartashov, Yu. Mishura
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International Journal of Stochastic Analysis, Volume 9, Issue 4, Page 415-426, 1996., 1996 Let {ζ(u), u ≥ 0} be a stochastic process with state space A ∪ B where A and B are disjoint sets. Denote by β(t) the total time spent in state B in the interval (0, t). This paper deals with the problem of finding the distribution of β(t) and the asymptotic distribution of β(t) as t → ∞ for various types of stochastic processes.
Lajos Takács
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International Journal of Stochastic Analysis, Volume 8, Issue 3, Page 209-232, 1995., 1995 In this paper explicit formulas are given for the distribution functions and the moments of the local times of the Brownian motion, the reflecting Brownian motion, the Brownian meander, the Brownian bridge, the reflecting Brownian bridge and the Brownian excursion.
Lajos Takács
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International Journal of Stochastic Analysis, Volume 7, Issue 3, Page 215-237, 1994., 1994 This paper, written in honor of the 70th birthday of Lajos Takács, addresses his life and work, and includes some personal observations and appreciation of his contributions. In particular, it includes a short biography, an informal discussion of some of his major research areas (queueing, fluctuations, waiting time processes, and random rooted trees),
Jewgeni H. Dshalalow, Ryszard Syski
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On the distribution of the number of vertices in layers of random trees
International Journal of Stochastic Analysis, Volume 4, Issue 3, Page 175-186, 1991., 1991 Denote by Sn the set of all distinct rooted trees with n labeled vertices. A tree is chosen at random in the set Sn, assuming that all the possible nn−1 choices are equally probable. Define τn(m) as the number of vertices in layer m, that is, the number of vertices at a distance m from the root of the tree. The distance of a vertex from the root is the
Lajos Takács
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Conditional limit theorems for branching processes
International Journal of Stochastic Analysis, Volume 4, Issue 4, Page 263-292, 1991., 1991 Let [ξ(m), m = 0, 1, 2, …] be a branching process in which each individual reproduces independently of the others and has probability pj(j = 0, 1, 2, …) of giving rise to j descendants in the following generation. The random variable ξ(m) is the number of individuals in the mth generation. It is assumed that P{ξ(0) = 1} = 1.
Lajos Takács
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