Pith Number

pith:M4Z36QMZ

pith:2026:M4Z36QMZD3FHM2SJHKZ6C32LL7

not attested not anchored not stored refs resolved

Mixed neural posterior estimation for simulators with discrete and continuous parameters

Cornelius Schr\"oder, Daniel Gedon, Jakob H. Macke, Jan Boelts, Jonas Beck, Michael Deistler

Neural posterior estimation extends to simulators with mixed discrete and continuous parameters through joint factorization and training.

arxiv:2605.13551 v1 · 2026-05-13 · cs.LG

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\usepackage{pith}
\pithnumber{M4Z36QMZD3FHM2SJHKZ6C32LL7}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across tractable toy examples and real-world scientific simulators, our joint inference approach yields accurate and calibrated posteriors.

C2weakest assumption

That the factorization of the joint posterior into discrete and continuous components, combined with joint training under a single simulation-based objective, will produce accurate and calibrated approximations without requiring additional constraints or post-training adjustments.

C3one line summary

Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.

References

15 extracted · 15 resolved · 1 Pith anchors

[1] Bayesian online changepoint detection · arXiv:0710.3742

[2] Investigating the impact of model misspecification in neural simulation-based inference.arXiv preprint arXiv:2209.01845,

[3] Simulation-based inference: A practical guide.arXiv preprint arXiv:2508.12939,

[4] Simultaneous identification of changepoints and model parameters in switching dynamical systems.bioRxiv, pp 2024

[5] Do diffusion models dream of electric planes? Discrete and continuous simulation-based inference for aircraft design.arXiv preprint arXiv:2603.13284,

Receipt and verification

First computed	2026-05-18T02:44:23.729876Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6733bf41991eca766a493ab3e16f4b5ff8360f28be46d9d456a7fff8dd72a335

Aliases

arxiv: 2605.13551 · arxiv_version: 2605.13551v1 · doi: 10.48550/arxiv.2605.13551 · pith_short_12: M4Z36QMZD3FH · pith_short_16: M4Z36QMZD3FHM2SJ · pith_short_8: M4Z36QMZ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/M4Z36QMZD3FHM2SJHKZ6C32LL7 \
  | 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: 6733bf41991eca766a493ab3e16f4b5ff8360f28be46d9d456a7fff8dd72a335

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d3ff1ff9874edd9da7cdaf87ba763612d33cff56e6a38e25f8655c0a854a3efd",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T13:57:27Z",
    "title_canon_sha256": "7189c539de416503214430ad811b835e35318de7eddbcae296009612a639295b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13551",
    "kind": "arxiv",
    "version": 1
  }
}