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• W2122507907 abstract "This paper illustrates novel methods for nonstationary time series modeling along with their applications to selected problems in neuroscience. These methods are semi-parametric in that inferences are derived by combining sequential Bayesian updating with a non-parametric change-point test. As a test statistic, we propose a Kullback–Leibler (KL) divergence between posterior distributions arising from different sets of data. A closed form expression of this statistic is derived for exponential family models, whereas standard Markov chain Monte Carlo output is used to approximate its value and its critical region for more general models. The behavior of one-step ahead predictive distributions under our semi-parametric framework is described analytically for a dynamic linear time series model. Conditions under which our approach reduces to fully parametric state-space modeling are also illustrated. We apply our methods to estimating the functional dynamics of a wide range of neural data, including multi-channel electroencephalogram recordings, longitudinal behavioral experiments and in-vivo multiple spike trains recordings. The estimated dynamics are related to the presentation of visual stimuli, to the evaluation of a learning performance and to changes in the functional connections between neurons over a sequence of experiments." @default.
• W2122507907 created "2016-06-24" @default.
• W2122507907 creator A5035211161 @default.
• W2122507907 creator A5039784236 @default.
• W2122507907 date "2009-12-01" @default.
• W2122507907 modified "2023-09-24" @default.
• W2122507907 title "Semi-parametric dynamic time series modelling with applications to detecting neural dynamics" @default.
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• W2122507907 doi "https://doi.org/10.1214/09-aoas275" @default.
• W2122507907 hasPublicationYear "2009" @default.
• W2122507907 type Work @default.
• W2122507907 sameAs 2122507907 @default.

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