Showing papers on "Particle filter published in 1970"
TL;DR: In this article, the problem of estimating the conditional mean of the posterior density function is formulated as a multidimensional integral and the control variate method presented shows that the Monte Carlo approach can successfully be adapted to estimate the approximation error of existing nonlinear filtering equations and to improve their accuracy significantly.
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TL;DR: A machine learning approach is adopted to devise variational Bayesian inference for variationalBayesian inference of random process generated by the autoregressive moving average (ARMA) linear model from non-linearity noise observations.
Abstract: Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noise observations. The posterior distribution of hidden states are approximated by a set of weighted particles generated by the sequential Monte carlo (SMC) algorithm involving sampling with importance sampling resampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model.