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2021
This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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1991
State space models may be regarded as generalizations of the models considered so far. They have been used extensively in system theory, the physical sciences, and engineering. The terminology is therefore largely from these fields. The general idea behind these models is that an observed (multiple) time series y 1 ,…, y T depends upon a possibly ...
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State space models may be regarded as generalizations of the models considered so far. They have been used extensively in system theory, the physical sciences, and engineering. The terminology is therefore largely from these fields. The general idea behind these models is that an observed (multiple) time series y 1 ,…, y T depends upon a possibly ...
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2017
The state space model is a very general model, mostly used to specify structural time-series models. Structural time-series models explicitly specify trends and seasonality along with other relevant influences. Under the classical Box-Jenkins time-series approach, in contrast, trends and seasonal influences are removed before estimating the core model.
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The state space model is a very general model, mostly used to specify structural time-series models. Structural time-series models explicitly specify trends and seasonality along with other relevant influences. Under the classical Box-Jenkins time-series approach, in contrast, trends and seasonal influences are removed before estimating the core model.
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2016
In this chapter, the state space model is thoroughly discussed. After defining the general state space model, the Kalman filter is derived. The square root covariance and the information filter are described. The topics of likelihood evaluation, forecasting, smoothing and covariance-based filters are discussed. Markov processes and the backwards Kalman
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In this chapter, the state space model is thoroughly discussed. After defining the general state space model, the Kalman filter is derived. The square root covariance and the information filter are described. The topics of likelihood evaluation, forecasting, smoothing and covariance-based filters are discussed. Markov processes and the backwards Kalman
openaire +1 more source
Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models
IEEE Transactions on Neural Networks and Learning Systems, 2022, Seyede Fatemeh Ghoreishi
exaly
Variational Bayes in State Space Models: Inferential and Predictive Accuracy
Journal of Computational and Graphical Statistics, 2023David T Frazier, Gael M Martin
exaly