Pith Number

pith:D3RWSQW7

pith:2026:D3RWSQW746Q35EB2BHJW4QGQ43

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Learning Adaptive Parameter Policies for Nonlinear Bayesian Filtering

Felipe Giraldo-Grueso, Ondrej Straka, Renato Zanetti

Reinforcement learning trains policies to choose filter parameters dynamically in nonlinear Bayesian estimation.

arxiv:2603.19910 v2 · 2026-03-20 · eess.SY · cs.SY

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Claims

C1strongest claim

Experiments with the unscented Kalman filter and stochastic integration filter demonstrate that the learned policies improve both estimate quality and consistency.

C2weakest assumption

That a reward function defined on estimation accuracy and consistency will produce policies that generalize beyond the training scenarios to real-world time-varying nonlinearity without retraining or instability.

C3one line summary

Reinforcement learning is used to learn adaptive policies for selecting parameters in nonlinear Bayesian filters, improving estimate quality and consistency in experiments with the unscented Kalman filter and stochastic integration filter.

References

33 extracted · 33 resolved · 0 Pith anchors

[1] S ¨arkk¨a,Bayesian Filtering and Smoothing 2013

[2] Unscented filtering and nonlinear estimation, 2004

[3] J. Dun ´ık, O. Straka, and M.ˇSimandl, “Stochastic integration filter,”IEEE Transactions on Automatic Control, vol. 58, no. 6, pp. 1561–1566, 2013 2013

[4] Sequential data assimilation with a nonlinear quasi- geostrophic model using Monte Carlo methods to forecast error statis- tics, 1994

[5] A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, 1999

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:22.643051Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1ee36942dfe7a1be903a09d36e40d0e6f043099d4addb596b00b562fd881166e

Aliases

arxiv: 2603.19910 · arxiv_version: 2603.19910v2 · doi: 10.48550/arxiv.2603.19910 · pith_short_12: D3RWSQW746Q3 · pith_short_16: D3RWSQW746Q35EB2 · pith_short_8: D3RWSQW7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/D3RWSQW746Q35EB2BHJW4QGQ43 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1ee36942dfe7a1be903a09d36e40d0e6f043099d4addb596b00b562fd881166e

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "80a3c93c5d47e4ae76ce5194ac663a7c187e840b6ab196e2d742fa2a6106634d",
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      "cs.SY"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-03-20T12:48:26Z",
    "title_canon_sha256": "73a19d27ce8cdbdf98a4c8691807c9827fcbfff0ee5e6c584c2cb7d0068a5edf"
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  "source": {
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