منابع مشابه
Switching State-Space Models
We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time series models|the hidden Markov model and the linear dynamical system|and is related to models that ar...
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State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex timeseries data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalabl...
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ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2011
ISSN: 1548-7660
DOI: 10.18637/jss.v041.i08