Journal ArticleDOI
Theory and Methods
TLDR
In this paper, a self-organizing filter and smoother for the general nonlinear non-Gaussian state-space model is proposed, which is defined by augmenting the state vector with the unknown parameters of the original state space model.Abstract:
A self-organizing filter and smoother for the general nonlinear non-Gaussian state-space model is proposed. An expanded state-space model is defined by augmenting the state vector with the unknown parameters of the original state-space model. The state of the augmented state-space model, and hence the state and the parameters of the original state-space model, are estimated simultaneously by either a non-Gaussian filter/smoother or a Monte Carlo filter/smoother. In contrast to maximum likelihood estimation of model parameters in ordinary state-space modeling, for which the recursive filter computation has to be done many times, model parameter estimation in the proposed self-organizing filter/smoother is achieved with only two passes of the recursive filter and smoother operations. Examples such as automatic tuning of dispersion and the shape parameters, adaptation to changes of the amplitude of a signal in seismic data, state estimation for a nonlinear state space model with unknown parameters, ...read more
Citations
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Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Journal ArticleDOI
Inference for nonlinear dynamical systems
TL;DR: This work presents a new method that makes maximum likelihood estimation feasible for partially-observed nonlinear stochastic dynamical systems (also known as state-space models) where this was not previously the case.
Journal ArticleDOI
A Survey of Sequential Monte Carlo Methods for Economics and Finance
TL;DR: The objective of this article is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.
Journal ArticleDOI
Parameter estimation in general state-space models
Arnaud Doucet,Vladislav B. Tadić +1 more
TL;DR: This work considers here an alternative approach combining particle filtering and gradient algorithms to perform batch and recursive maximum likelihood parameter estimation.
Journal ArticleDOI
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
Seiya Imoto,Sun Yong Kim,Takao Goto,Sachiyo Aburatani,Kousuke Tashiro,Satoru Kuhara,Satoru Miyano +6 more
TL;DR: A new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network is proposed and a new graph selection criterion from Bayesian approach in general situations is theoretically derived.
References
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Journal ArticleDOI
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
TL;DR: A new algorithm based on a Monte Carlo method that can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low.