
A hybrid particleensemble Kalman filter for problems with medium nonlinearity
A hybrid particle ensemble Kalman filter is developed for problems with ...
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Probabilistic PARAFAC2
The PARAFAC2 is a multimodal factor analysis model suitable for analyzin...
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Identifying latent groups in spatial panel data using a Markov random field constrained product partition model
Understanding the heterogeneity over spatial locations is an important p...
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MaxandSmooth: a twostep approach for approximate Bayesian inference in latent Gaussian models
With modern highdimensional data, complex statistical models are necess...
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Meanfield limit and numerical analysis for Ensemble Kalman Inversion: linear setting
Ensemble Kalman inversion (EKI) is a method introduced in [14] to find s...
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Design of a Simple Orthogonal Multiwavelet Filter by Matrix Spectral Factorization
We consider the design of an orthogonal symmetric/antisymmetric multiwav...
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Modeling Material Stress Using Integrated Gaussian Markov Random Fields
The equations of a physical constitutive model for material stress withi...
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Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data
We introduce the Gaussian orthogonal latent factor processes for modeling and predicting large correlated data. To handle the computational challenge, we first decompose the likelihood function of the Gaussian random field with multidimensional input domain into a product of densities at the orthogonal components with lower dimensional inputs. The continuoustime Kalman filter is implemented to efficiently compute the likelihood function without making approximation. We also show that the posterior distribution of the factor processes are independent, as a consequence of prior independence of factor processes and orthogonal factor loading matrix. For studies with a large sample size, we propose a flexible way to model the mean in the model and derive the closedform marginal posterior distribution. Both simulated and real data applications confirm the outstanding performance of this method.
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