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Add effective sample size to Population #268
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I assume this preserves the unbiased nature of SMC (since no resampling is unbiased, and full resampling is unbiased) - if not it should be commented.
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I assume this preserves the unbiased nature of SMC (since no resampling is unbiased, and full resampling is unbiased) - if not it should be commented.
I'm not sure. Is there a resource to read up about biases introduced by resampling? I didn't read anything in Murphy's book about this not being unbiased. I would have thought that if the procedure at each timestamp is not biased, the whole process is not biased, but I can't prove it. |
Also, I want to add a fixture test and look at the values a bit before merging. |
I don't really know, but yeah, I also agree with that reasoning. |
I managed to produce a crash using this function in |
The crash was:
I could not manage to reproduce it again. In subsequent runs, it seemed to me that one could definitely speed up performance, while keeping the same variance. |
-- | The new resampler | ||
(Population m a -> Population m a) | ||
onlyBelowEffectiveSampleSize threshold resampler pop = do | ||
ess <- lift $ effectiveSampleSize pop |
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I think this is wrong because it will execute the m
effects of pop
again.
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It might be better to have a function withEffectiveSampleSize :: Functor m => Population m a -> m (a, Double)
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@@ -206,6 +208,31 @@ resampleMultinomial :: | |||
PopulationT m a | |||
resampleMultinomial = resampleGeneric multinomial | |||
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-- | Only use the given resampler when the effective sample size is below a certain threshold | |||
onlyBelowEffectiveSampleSize :: |
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I should have one fixture test and a unit test where this is used.
I recently read about improving existing resampling methods by only resampling when the effective sample size is small.