Umberto Picchini
Inference for SDE models via Approximate Bayesian Computation.
Tue, 05/01/2012 - 15:01 — SomNambulAuthors: Umberto Picchini
Subjects: Methodology
AbstractModels defined by stochastic differential equations (SDEs) allow for the
representation of random variability in dynamical systems. The relevance of
this class of models is growing in many applied research areas and is already a
standard tool to model e.g. financial, neuronal and population growth dynamics.
However inference for multidimensional SDE models is still very challenging
from a computational and theoretical point of view.- Read more
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Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models.
Tue, 10/05/2010 - 15:01 — SomNambulAuthors: Umberto Picchini, Susanne Ditlevsen
Subjects: Computation
AbstractStochastic differential equations (SDEs) are established tools to model
physical phenomena whose dynamics are affected by random noise. By estimating
parameters of an SDE intrinsic randomness of a system around its drift can be
identified and separated from the drift itself. When it is of interest to model
dynamics within a given population, i.e. to model simultaneously the
performance of several experiments or subjects, mixed-effects modelling allows
for the distinction of between and within experiment variability.
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