# RMSE vague in 2D particle filtering with LowLevelParticleFilters.jl?

**URL:** <https://discourse.julialang.org/t/rmse-vague-in-2d-particle-filtering-with-lowlevelparticlefilters-jl/131042>\
**Category:** Modelling & Simulations\
**Tags:** question\
**Created:** [July 25, 2025, 5:06am UTC](https://discourse.julialang.org/t/rmse-vague-in-2d-particle-filtering-with-lowlevelparticlefilters-jl/131042 "2025-07-25T05:06:33Z")\
**Posts on this page:** 1\
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**Author:** ![baggepinnen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/baggepinnen/32/693_2.png) [@baggepinnen](https://discourse.julialang.org/u/baggepinnen)\
**Post date:** [July 25, 2025, 6:37am UTC](https://discourse.julialang.org/t/rmse-vague-in-2d-particle-filtering-with-lowlevelparticlefilters-jl/131042/2 "2025-07-25T06:37:13Z")

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See answer here

> [@Particle Filtering methods](https://discourse.julialang.org/t/particle-filtering-methods/130828/10):
>
> It depends on how large the uncertainty is in the initial distribution compared to the stationary distribution. If you push that example much further, it might converge. You can compute the stationary covariance matrix by solving a Riccati equation with the A jacobian, see [Influence of sample rate on performance · LowLevelParticleFilters Documentation](https://baggepinnen.github.io/LowLevelParticleFilters.jl/stable/sample_rate/) for some details or “Asymptotic form” in [Kalman filter - Wikipedia](https://en.wikipedia.org/wiki/Kalman_filter#Asymptotic_form) using MatrixEquations A = Ajac(...) C = Cjac(...) R\_stationary, \_ = ared(A, C'…

particle filters add additional monte-carlo approximation error that makes the analysis slightly harder.

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