[Todos CMAT] Seminario de Probabilidad y Estadística -- Viernes 31 de julio

[Andrés Sosa]

Hola a todos

Este viernes 31 de julio a las 10:00 horas en el salón de seminarios del
Centro de Matemática hablará* Álvaro Moraes  *(King Abdullah University of
Science and Technology --  KAUST) en el seminario de Probabilidad y
Estadística.

Referencias a esta charla se encuentra en:
http://sri-uq.kaust.edu.sa/Pages/Moraes.aspx

Saludos
Andrés

*Abstract:*







*In this talk, we present a novel multilevel Monte Carlo method for kinetic
simulation of stochastic reaction networks characterized by  fast and slow
reaction channels. To produce efficient simulations, we automatically
classify the reactions channels into the fast and slow classes. To this
end, we first introduce the concept of the level of activity of a reaction
channel, which depends on the current state of the system.Then, we propose
a low cost heuristic that allows us to adaptively split the set of reaction
channels into two subsets characterized by either a high or low level of
activity. Based on a time splitting technique, the increments associated
with high activity channels are simulated using the tau-leap method while
those associated with low activity channels are simulated using an exact
method. This path simulation technique, which we name mixed method, is
amenable for coupled path generation and a corresponding multilevel  Monte
Carlo algorithm.To estimate expected values of observables of the system at
a prescribed final time, our method bounds the global computational error
to be below a prescribed tolerance, TOL, within a given confidence level.
This goal is achieved with a  computational complexity of order
O(TOL^(-2)), the same as with a pathwise exact method, but with a smaller
constant. Time permitting, we want to present a novel control variate
technique based on the stochastic time change representation by Kurtz,
which may dramatically reduce the variance of the coarsest level at a
negligible computational cost. Our numerical examples show substantial
gains with respect to the standard Stochastic Simulation Algorithm (SSA).*
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