Work

Identiﬁability of Finite Mixture Models with underlying Normal Distribution

Year: 2020

Type: report

Abstract: In this paper, we show under which conditions generalized ﬁnite mixture with underlying normal distribution are identiﬁable in the sense that a given dataset leads to a uniquely determined set of model parameter estimations up to a permuta-tion of the clusters.

Source: Open Repository and Bibliography (University of Luxembourg)

Authors: NOEL, Cédric, SCHILTZ, Jang

Institutions: Université de Lorraine, University of Luxembourg

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7000984545

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