Comments (3)
Having
There is no way to access state older than the last step, the definition of state is the complete set of information that is required to predict the future, hence,
One could implement a custom particle filter to handle this case if the use of memory becomes a bottleneck, but his would have to special case not only the dynamics update, but also handling of the initial state etc. since you'd need multiple past values of
A further complication would be in the resampling step, since it would not be enough to resample the current value of
BTW, this model is linear, is the noise Gaussian? If so, I'd use a Kalman filter instead
from lowlevelparticlefilters.jl.
Cool, I'll close this issue for now, feel free to continue the discussion if you have further questions :)
from lowlevelparticlefilters.jl.
Thanks for the explanation! I'll then proceed as you suggested.
The noise in my current dynamics is not always Gaussian, since I have some parameters constrained to certain intervals. But later on I might simplify my model to be able to use Kalman filter.
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Related Issues (20)
- Struct of Arrays and BLAS2->3 optimization HOT 1
- Dependency Deprecated - Yeppp HOT 9
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- LowLevelParticleFilters won't compile on Julia 1.8/macOS HOT 4
- Passing dynamics noise density makes stochastic dynamics less expressive HOT 3
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- Observed variables HOT 2
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from lowlevelparticlefilters.jl.