29,911 research outputs found
Online estimation of discrete densities using classifier chains
We propose an approach to estimate a discrete joint density online, that is, the algorithm is only provided the current example, its current estimate, and a limited amount of memory. To design an online estimator for discrete densities, we use classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains. Our experiments on synthetic data show that the approach is feasible and the estimated densities approach the true, known distribution with increasing amounts of data
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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