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Bayesian model-based change detection
Maintain the sample mean and covariance of the response variables inside a private data member called __statistics
. Every time the update
method is called, possibly with missing values (i.e. numpy.nan
) in the response matrix Y
, do the following:
self.__statistics
(if there are no missing values, then the mean is Y
and the covariance is zero);self.__statistics
incrementally using this posterior mean and covariance; andself.__hypotheses
by passing in the stored basis functions evaluated at X
, and posterior mean and covariance over Y
.Improve README.md
and add pointers to test scripts.
Rather than just dropping the particles with the smallest weights, implement stratified optimal resampling according to page 10 of Fearnhead and Liu (2007).
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