Nonlinear state-space models
Stochastic Gradient MCMC for Nonlinear State Space Models
... Nonlinear state space models (SSMs) are widely used in many scientific domains for modeling time series and sequential ...example, nonlinear SSMs can be applied in engineering ...
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Monte Carlo fixed-lag smoothing in state-space models
... Monte Carlo sequential methods, contrary to standard Kalman filters, are able to deal with the filtering prob- lem in nonlinear state-space models. The particle filtering (Del Moral et al., ...
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A universal U model based control system design
... polynomial models are frequently obtained from principle and/or data driven identification, particularly in the cases of single-input and single-output (SISO) dynamic plants, even though maybe with complex ...
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State-space approach to nonlinear predictive generalized minimum variance control
... PGMV Optimal Controller Consider the linear components of the plant, disturbance and output weighting models in state space form 2, 3 with input from the nonlinear finite gain stable pla[r] ...
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Nonlinear Markov games on a finite state space (mean field and binary interactions)
... initial state space was finite, resulting in the corresponding measure-valued limit being a finite- dimensional di ff erential ...initial state space of a single particle (in our large ...
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The first stage studies of U state space control system design
... two nonlinear discrete time state space models are selected to test the proposed ...these models to demonstrate that the proposed method is generally suitable for controlling different ...
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Contribution in Information Signal Processing for Solving State Space Nonlinear Estimation Problems
... the state space model input is a combination of several sen- sors as in data fusion or multi-sensors fusion, it was proven that comparing with Kalman filter and extended Kalman filter both for linear and ...
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Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation
... One of the unfortunate facts facing data analysts is miss- ing data. Data that are known to have been observed erro- neously can be categorized as missing. Erroneous data can also wreak havoc with the estimation and ...
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The state space models toolbox for MATLAB
... a state space matrix, with elements marked as variable (dependent on model parameters) and/or dynamic (dependent on ...and nonlinear functions, ...and nonlinear model elements by linear ...
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Dynamic State Space Models
... dynamic state-space model was developed in the control systems literature, where physi- cal systems are described mathematically as sets of inputs, outputs, and state variables, related by difference ...
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Fast sequential parameter inference for dynamic state space models
... dynamic state-space model generally have a non- Gaussian posterior distribution and holds a nonlinear relationship with the ...the state-space model are used to sequentially update the ...
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Maximum likelihood estimation of time series models: the Kalman filter and beyond
... introduces state space models and provides the state space representation of some commonly applied linear processes, such as univariate and multivariate autoregressive moving average ...
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Modeling Exchange Rate Dynamics in Egypt: Observed and Unobserved Volatility
... TVP models for the first time on the Egyptian case. These models, although fashionable, have become a standard and popular modeling framework for economic and financial analysis of time ...traditional ...
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PubMedCentral-PMC5378168.pdf
... a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive ...ROI state loadings, and one had a time-varying ...
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Modeling Monthly Rainfall Time Series Using Ets State Space And Sarima Models
... There are different categories of predictive modeling; the causal method and the time series method. The causal method t the predicted variable is determined by independent explanatory variables, that is regression. The ...
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Auxiliary likelihood based approximate Bayesian computation in state space models
... Finally, we note that despite the focus of this paper being on inference about the static parameters in the state space model, there is nothing to preclude marginal inference on the states being conducted, ...
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Bootstrap Approximation to Prediction MSE for State Space Models with Estimated Parameters
... the state vector predictors when the unknown model parameters are estimated from the observed ...in state-space modelling are inadequate and may not even be operational when fitting complex ...
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Dynamic probabilities of restrictions in state space models: an application to the Phillips curve
... Φορ ινστανχε, ιφ Χ ισ τηε στανδαρδ …ρστ−δι¤ερενχε mατριξ ανδ χ = 0 τηεν τηε ρεστριχτιονσ οφ ιντερεστ αρε 1 = 2 = :: = Τ ανδ τηε παραmετερσ αρε νοτ τιmε ϖαρψινγ αλτηουγη, ασ νοτεδ ιν τηε [r] ...
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Particle Filters and Data Assimilation
... An alternative approach is to use MCMC moves within the particle filter to sample new parameter values for each particle using an MCMC kernel that has the current posterior dis- tribution as its invariant distribution. ...
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Nonlinear Analysis of the BOLD Signal
... the state functions and system parameters of the hemodynamic approach for the superior parietal ...estimated state functions of the hemodynamic response to touch perception ...
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