1994
DOI: 10.1007/978-3-642-57913-4
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Numerical Solution of SDE Through Computer Experiments

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

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Cited by 411 publications
(436 citation statements)
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“…In this case, the transition probability density function of the process is (12) which corresponds to a lognormal distribution, that is,…”
Section: Probability Distribution and Some Characteristics Of The Promentioning
confidence: 99%
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“…In this case, the transition probability density function of the process is (12) which corresponds to a lognormal distribution, that is,…”
Section: Probability Distribution and Some Characteristics Of The Promentioning
confidence: 99%
“…To this end, we have simulated sample paths from the von Bertalanffy diffusion process following the algorithm derived from the numerical solution of the stochastic differential equation associated with the process (see Kloeden et al [12]). In our case, the algorithm is 14 A c c e p t e d m a n u s c r i p t…”
Section: Simulation Studymentioning
confidence: 99%
“…Se a função escolhida for f = L 1 b(X t ), a expansão já se torna mais complexa apresentando uma integral estocástica dupla. (KLOEDEN et al, 2003) …”
Section: Fórmula De Itôunclassified
“…A principal diferença em relação ao método de Euler para equações diferenciais ordinárias é a presença dos incrementos aleatórios ∆W n . Para a aplicação do método, ele deve ser gerado para cada passo da discretização temporal, seguindo sua definição: são variáveis aleatórias Gaussianas independentes com média igual a zero e variância igual a ∆t, que neste caso é h. (KLOEDEN et al, 2003) Há dois tipos de convergência para soluções numéricas de equações diferenciais estocásticas: forte e fraca. Convergência forte da ordem α é definida por…”
Section: Solução Numérica De Sdesunclassified
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