probnum.filtsmooth¶
Bayesian Filtering and Smoothing.
This package provides different kinds of Bayesian filters and smoothers which estimate the distribution over observed and hidden variables in a sequential model. The two operations differ by what information they use. Filtering considers all observations up to a given point, while smoothing takes the entire set of observations into account.
Functions¶
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Approximate effective number of events in the support of a categorical random variable. |
Classes¶
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Bayesian filtering and smoothing. |
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Gaussian filtering and smoothing, i.e. Kalman-like filters and smoothers. |
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Interface for extended Kalman filtering components. |
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Continuous-time extended Kalman filter transition. |
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Discrete extended Kalman filter transition. |
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Interface for unscented Kalman filtering components. |
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Continuous-time unscented Kalman filter transition. |
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Discrete unscented Kalman filter transition. |
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Used for unscented Kalman filter. |
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Posterior Distribution over States after time-series algorithms such as filtering/smoothing or solving ODEs. |
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Posterior distribution after approximate Gaussian filtering and smoothing. |
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Filtering posterior. |
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Smoothing posterior. |
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Stop iteration if absolute and relative tolerance are reached. |
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Iterated updates. |
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Particle filter (PF). |
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Posterior distribution of a particle filter.. |