Profile cut-off phenomenon for the ergodic Feller root process Gerardo Barrera, Liliana Esquivel May 2025 View PDF 15 June 2017 Read the most downloaded articles in Stochastic Processes and their Applications 22 May 2017 View all news Calls for papers ...
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STOCHASTIC PROCESSES AND THEIR APPLICATIONS 3 2 N D C O N F E R E N C E | A U G U S T 6 – 1 0 , 2 0 0 7 UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN D E P A R T M E N T O F M A T H E M A T I C S i Welcome to the 32nd Conference on Stochastic Processe...
Stochastic Processes and Their Applications Proceedings of the International Conference held in Nagoya, July 2-6, 1985Conference proceedings © 1986 Overview Editors: Kiyosi Itô, Takeyuki Hida Part of the book series: Lecture Notes in Mathematics (LNM, volume 1203) 10k Accesses ...
Stochastic Processes and their Applications 78 (1998) 69--95 Large deviations for interacting particle systems: Applications to non-linear ltering The non-linear #ltering problem consists in computing the conditional distributions of a Markov signal process given its noisy observations. The dynamical ....
STOCHASTIC PROCESSES AND THEIR APPLICATIONS IN MEDICAL SCIENCE A stochastic processX= {X(t),t∈T}is at-indexed collection of random variables. That is to say, for anyt∈T,X(t) is a random variable.tis a parameter. We often interprettas time. If the setTis a countable set, we call ...
Functional-series analysis of bilinear systems driven by White Gaussian process : Syozo Yasui, National Institute for Basic Biology, Okazaki, Japan Page 21 View PDF select article Deterministic and stochastic epidemics with several kinds of susceptibles : Frank Ball, University of Nottingham, England ...
Brownian motion (BM) has been widely used for degradation modeling and remaining useful life (RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some ...
A stochastic integrator is a linear map which is defined on the set of simple previsible processes and which admits a continuous extension to a class containing all bounded previsible processes. If the map H→H·X (with H simple and previsible) is a stochastic integrator, the process X ...
Accordingly, we use the Gibbs–Shannon entropy to quantify the inferrability of a system36, and later on show that this indeed is a good measure. Given a stochastic process, Xt, characterized by its transition matrix Tα(ω) = Pr(Xt = ω|X0 = α), and initial state α...