Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations

(2012) Ke Yuan et al.   NEURAL COMPUTATION

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

(2011) Finn Lindgren et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Online Variational Inference for State-Space Models with Point-Process Observations

(2011) Andrew Zammit Mangion et al.   NEURAL COMPUTATION

A data-driven framework for neural field modeling

(2011) D.R. Freestone et al.   NEUROIMAGE

Approximate Methods for State-Space Models

(2010) Shinsuke Koyama et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation

(2010) Marc Bocquet et al.   MONTHLY WEATHER REVIEW

Estimating a State-Space Model from Point Process Observations: A Note on Convergence

(2010) Ke Yuan et al.   NEURAL COMPUTATION

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models

(2009) J. Daunizeau et al.   PHYSICA D-NONLINEAR PHENOMENA

Estimation and Model Selection for an IDE-Based Spatio-Temporal Model

(2008) K. Scerri et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING

Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

(2008) M. Dewar et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING