Comments (4)
We do not support missing values for all filters at this point. The docs do not intend to claim that we support missing values, if that's your interpretation of the "Dropped samples" section it needs some clarification :) The docs are simply trying to say that supporting missing values in your own filter loop is trivial.
from lowlevelparticlefilters.jl.
Thank you! That make sense, I was just a bit confused.
Just one more quick question for clarification. How do you interpret the short circuit evaluation mentioned above?
from lowlevelparticlefilters.jl.
How do you interpret the short circuit evaluation mentioned above?
As you say, it's there to handle missing measurements. The problem is that not all filters have this mechanism implemented yet. The method used there is also not always the optimal way to handle missing measurements. In cases when you have more than one sensor, you can still perform the measurement update with a subset of the measurements if some but not all are missing.
from lowlevelparticlefilters.jl.
Okay, thank you for the clarification!
from lowlevelparticlefilters.jl.
Related Issues (20)
- Struct of Arrays and BLAS2->3 optimization HOT 1
- Dependency Deprecated - Yeppp HOT 9
- TagBot trigger issue HOT 32
- notation + documentation HOT 2
- v3.0
- Ensemble Kalman filters? HOT 2
- Support for non-uniform observations? HOT 8
- Typo in DAEUnscentedKalmanFilter docstring
- LowLevelParticleFilters won't compile on Julia 1.8/macOS HOT 4
- Passing dynamics noise density makes stochastic dynamics less expressive HOT 3
- Incremental precomiplation fatally broken HOT 1
- More flexible noise in UKF
- Next major version
- Compatibility to ModelingToolkit.jl? HOT 4
- Observed variables HOT 2
- Installing this package is downgrading many other packages
- Improve documentation HOT 4
- Particle filter with second order Markov model HOT 3
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from lowlevelparticlefilters.jl.