pith:L6YNKTIO
Parameter estimation for kappa distributions using the EM algorithm in the superstatistical framework
Modeling inverse temperature as a gamma-distributed latent variable recovers the exponential family structure for kappa distributions and yields a closed-form EM algorithm for parameter estimation.
arxiv:2605.05428 v2 · 2026-05-06 · stat.ME · cond-mat.stat-mech · physics.plasm-ph
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Claims
This enables an implementation of the expectation-maximization (EM) algorithm in analytically closed form, with E-step and M-step derived from sufficient statistics.
The inverse temperature β is introduced as a gamma-distributed latent variable that recovers the exponential family structure in the complete-data likelihood.
Data augmentation with a gamma latent inverse temperature recovers the exponential family for kappa distributions, allowing a closed-form EM algorithm for maximum likelihood estimation in the superstatistics framework.
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| First computed | 2026-05-22T01:03:19.815118Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5fb0d54d0e9f305aababc45d43e1d3bd412663f57c449382d4ec3e9eaa7441c4
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/L6YNKTIOT4YFVK5LYROUHYOTXV \
| 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())"
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Canonical record JSON
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