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object --+ | Model
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Inherited from |
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Inherited from |
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x.__init__(...) initializes x; see x.__class__.__doc__ for signature
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The process model for a first-order auto-regressive process is: x_{t+1} - mean = (x_t - mean)*scaling + sigma*w_t where w_t is unit iid Gaussian noise.
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This routine samples from the distribution p(x_t | x_{t-1} = oldpos[old_sample]) and stores the result in new_positions[new_sample]. This is straightforward for the simple first-order auto-regressive process model used here, but any model could be substituted. |
This routine evaluates the observation density p(z_t|x_t = newpos[new_sample]) The observation model in this implementation is a simple mixture of Gaussians, where each simulated object is observed as a 1d position and measurement noise is represented as Gaussian. For a visual-tracking application, this routine would go and evaluate the likelihood that the object is present in the image at the position encoded by new_positions[new_sample]. |
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Generated by Epydoc 3.0.1 on Tue Jul 28 13:42:38 2009 | http://epydoc.sourceforge.net |