1. Introduction Z. Chen; 2. Inference and learning in latent Markov models D. Barber and S. Chiappa; Part I. State Space Methods for Neural Data: 3. State space methods for MEG source reconstruction M. Fukushima, O. Yamashita and M. Sato; 4. Autoregressive modeling of fMRI time series: state space approaches and the general linear model A. Galka, M. Siniatchkin, U. Stephani, K. Groening, S. Wolff, J. Bosch-Bayard and T. Ozaki; 5. State space models and their spectral decomposition in dynamic causal modeling R. Moran; 6. Estimating state and parameters in state space models of spike trains J. H. Macke, L. Buesing and M. Sahani; 7. Bayesian inference for latent stepping and ramping models of spike train data K. W. Latimer, A. C. Huk and J. W. Pillow; 8. Probabilistic approaches to uncover rat hippocampal population codes Z. Chen, F. Kloosterman and M. A. Wilson; 9. Neural decoding in motor cortex using state space models with hidden states W. Wu and S. Liu; 10. State-space modeling for analysis of behavior in learning experiments A. C. Smith; Part II. State Space Methods for Clinical Data: 11. Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring L. H. Lehman, M. J. Johnson, S. Nemati, R. P. Adams and R. G. Mark; 12. Identifying outcome-discriminative dynamics in multivariate physiological cohort time series S. Nemati and R. P. Adams; 13. A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions Z. Chen and R. Barbieri; 14. Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression M. B. Westover, S. Ching, M. M. Shafi, S. S. Cash and E. N. Brown; 15. Signal quality indices for state-space electrophysiological signal processing and vice versa J. Oster and G. D. Clifford.
1. Introduction Z. Chen; 2. Inference and learning in latent Markov models D. Barber and S. Chiappa; Part I. State Space Methods for Neural Data: 3. State space methods for MEG source reconstruction M. Fukushima, O. Yamashita and M. Sato; 4. Autoregressive modeling of fMRI time series: state space approaches and the general linear model A. Galka, M. Siniatchkin, U. Stephani, K. Groening, S. Wolff, J. Bosch-Bayard and T. Ozaki; 5. State space models and their spectral decomposition in dynamic causal modeling R. Moran; 6. Estimating state and parameters in state space models of spike trains J. H. Macke, L. Buesing and M. Sahani; 7. Bayesian inference for latent stepping and ramping models of spike train data K. W. Latimer, A. C. Huk and J. W. Pillow; 8. Probabilistic approaches to uncover rat hippocampal population codes Z. Chen, F. Kloosterman and M. A. Wilson; 9. Neural decoding in motor cortex using state space models with hidden states W. Wu and S. Liu; 10. State-space modeling for analysis of behavior in learning experiments A. C. Smith; Part II. State Space Methods for Clinical Data: 11. Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring L. H. Lehman, M. J. Johnson, S. Nemati, R. P. Adams and R. G. Mark; 12. Identifying outcome-discriminative dynamics in multivariate physiological cohort time series S. Nemati and R. P. Adams; 13. A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions Z. Chen and R. Barbieri; 14. Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression M. B. Westover, S. Ching, M. M. Shafi, S. S. Cash and E. N. Brown; 15. Signal quality indices for state-space electrophysiological signal processing and vice versa J. Oster and G. D. Clifford.
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