10,524 research outputs found
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation methods can be used to evaluate posterior
distributions without having to calculate likelihoods. In this paper we discuss
and apply an approximate Bayesian computation (ABC) method based on sequential
Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC
SMC gives information about the inferability of parameters and model
sensitivity to changes in parameters, and tends to perform better than other
ABC approaches. The algorithm is applied to several well known biological
systems, for which parameters and their credible intervals are inferred.
Moreover, we develop ABC SMC as a tool for model selection; given a range of
different mathematical descriptions, ABC SMC is able to choose the best model
using the standard Bayesian model selection apparatus.Comment: 26 pages, 9 figure
Linear and nonlinear filtering in mathematical finance: a review
Copyright @ The Authors 2010This paper presents a review of time series filtering and its applications in mathematical finance. A summary of results of recent empirical studies with market data are presented for yield curve modelling and stochastic volatility modelling. The paper also outlines different approaches to filtering of nonlinear time series
Online Sequential Monte Carlo smoother for partially observed stochastic differential equations
This paper introduces a new algorithm to approximate smoothed additive
functionals for partially observed stochastic differential equations. This
method relies on a recent procedure which allows to compute such approximations
online, i.e. as the observations are received, and with a computational
complexity growing linearly with the number of Monte Carlo samples. This online
smoother cannot be used directly in the case of partially observed stochastic
differential equations since the transition density of the latent data is
usually unknown. We prove that a similar algorithm may still be defined for
partially observed continuous processes by replacing this unknown quantity by
an unbiased estimator obtained for instance using general Poisson estimators.
We prove that this estimator is consistent and its performance are illustrated
using data from two models
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