<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>4.	Nurvita Trianasari, I Made Sumertajaya, Erfiani, I Wayan Mangku, Bivariate Beta Mixture Model with Correlations, sudah di publish pada Journal Communications in Mathematical Biology and Neuroscience (CMBN) (Q3 Scopus), Vol.2021, ID 26, tahun 2021.</titl><IDNo agency="DOI">doi:10.34820/FK2/34PIEQ</IDNo></titlStmt><distStmt><distrbtr source="archive">Telkom University Dataverse</distrbtr><distDate>2023-10-05</distDate></distStmt><verStmt source="archive"><version date="2023-10-05" type="RELEASED">1</version></verStmt><biblCit>Trianasari, Nurvita, 2023, "4. Nurvita Trianasari, I Made Sumertajaya, Erfiani, I Wayan Mangku, Bivariate Beta Mixture Model with Correlations, sudah di publish pada Journal Communications in Mathematical Biology and Neuroscience (CMBN) (Q3 Scopus), Vol.2021, ID 26, tahun 2021.", https://doi.org/10.34820/FK2/34PIEQ, Telkom University Dataverse, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>4.	Nurvita Trianasari, I Made Sumertajaya, Erfiani, I Wayan Mangku, Bivariate Beta Mixture Model with Correlations, sudah di publish pada Journal Communications in Mathematical Biology and Neuroscience (CMBN) (Q3 Scopus), Vol.2021, ID 26, tahun 2021.</titl><IDNo agency="DOI">doi:10.34820/FK2/34PIEQ</IDNo></titlStmt><rspStmt><AuthEnty affiliation="telkom university">Trianasari, Nurvita</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Telkom University Dataverse</distrbtr><contact affiliation="telkom university" email="nurvitatrianasari@telkomuniversity.ac.id">Trianasari, Nurvita</contact><depositr>Trianasari, Nurvita</depositr><depDate>2022-09-22</depDate></distStmt></citation><stdyInfo><subject><keyword>Mathematical Sciences</keyword><keyword>Keywords: probabilistic clustering, MLE method, EM algorithm, ICL-BIC, Monte Carlo Simulation.</keyword></subject><abstract date="2021-03-30">The method of clustering is a probabilistic model based on clustering technique.  The clustering method is often based on the assumption that data comes from a mixed model.  One such mixture model is the beta mixed model.  This mixed model can be used for the case of one variable or multiple variables.  However, for the mixed beta model of the double variable, each variable is assumed to be independent.  In this article, we propose a mixed beta model with correlated variables.  The parameter estimation method uses the MLE method via the EM algorithm.  While determining the optimal number of clusters using the ICL-BIC criteria.  Monte Carlo simulation is used to see the performance of the model.</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><notes type="DVN:TOU" level="dv">CC0 Waiver</notes><setAvail/><useStmt/></dataAccs><othrStdyMat/></stdyDscr><otherMat ID="f10005" URI="https://doi.org/10.34820/FK2/34PIEQ/3EBCRF" level="datafile"><labl>DATAVERSE_NVT.xlsx</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/vnd.openxmlformats-officedocument.spreadsheetml.sheet</notes></otherMat></codeBook>