Inference through innovation processes tested in the authorship attribution task

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00599642" target="_blank" >RIV/67985807:_____/24:00599642 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1038/s42005-024-01714-6" target="_blank" >https://doi.org/10.1038/s42005-024-01714-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s42005-024-01714-6" target="_blank" >10.1038/s42005-024-01714-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Inference through innovation processes tested in the authorship attribution task

Popis výsledku v původním jazyce

Urn models for innovation capture fundamental empirical laws shared by several real-world processes. The so-called urn model with triggering includes, as particular cases, the urn representation of the two-parameter Poisson-Dirichlet process and the Dirichlet process, seminal in Bayesian non-parametric inference. In this work, we leverage this connection to introduce a general approach for quantifying closeness between symbolic sequences and test it within the framework of the authorship attribution problem. The method demonstrates high accuracy when compared to other related methods in different scenarios, featuring a substantial gain in computational efficiency and theoretical transparency. Beyond the practical convenience, this work demonstrates how the recently established connection between urn models and non-parametric Bayesian inference can pave the way for designing more efficient inference methods. In particular, the hybrid approach that we propose allows us to relax the exchangeability hypothesis, which can be particularly relevant for systems exhibiting complex correlation patterns and non-stationary dynamics.

Název v anglickém jazyce

Inference through innovation processes tested in the authorship attribution task

Popis výsledku anglicky

Urn models for innovation capture fundamental empirical laws shared by several real-world processes. The so-called urn model with triggering includes, as particular cases, the urn representation of the two-parameter Poisson-Dirichlet process and the Dirichlet process, seminal in Bayesian non-parametric inference. In this work, we leverage this connection to introduce a general approach for quantifying closeness between symbolic sequences and test it within the framework of the authorship attribution problem. The method demonstrates high accuracy when compared to other related methods in different scenarios, featuring a substantial gain in computational efficiency and theoretical transparency. Beyond the practical convenience, this work demonstrates how the recently established connection between urn models and non-parametric Bayesian inference can pave the way for designing more efficient inference methods. In particular, the hybrid approach that we propose allows us to relax the exchangeability hypothesis, which can be particularly relevant for systems exhibiting complex correlation patterns and non-stationary dynamics.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA21-17211S" target="_blank" >GA21-17211S: Síťové modely komplexních systémů: od korelačních grafů k informačním hypergrafům</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMMUNICATIONS PHYSICS

ISSN

2399-3650

e-ISSN

2399-3650

Svazek periodika

7

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

300

Kód UT WoS článku

001306596800002

EID výsledku v databázi Scopus

2-s2.0-85203270503