## 8 Conclusions  
We provided an algorithm in the Bayesian mixture analysis. We called it non-Gaussian reversible jump Markov chain Monte Carlo (NG-RJMCMC). Our algorithm is a modification of RJMCMC, where there is a difference in the initial steps, namely changing the original distribution into a location-scale family. This step facilitates the grouping of each observation into the mixture components. Our algorithm allows researchers to easily analyze data that are not from the Gaussian family. In our study, we used Weibull distribution, then transformed it into the EV-I distribution.  
To validate our algorithm, we performed 16 scenarios for the EV-I mixture distribution simulation study. The first to fourth scenarios had two components, the fifth to eighth scenarios had three components, the ninth to twelfth scenarios had four components, and the thirteenth to sixteenth scenarios had five components. We generated data in different sizes, ranging from 125 to 1000 samples per mixture component. Next, we analyzed them using a Bayesian analysis with the appropriate prior distributions. We used 200,000 sweeps per scenario and replicated them 500 times. The results of this simulation indicate that each scenario provides a minimum level of accuracy of 95%. Moreover, the estimated parameters come close to the real parameters for all scenarios.  
To strengthen the proposed method, we provided misspecification cases. We deliberately generated unimodal data with double-exponential and logistic distributions, then estimated them using the EV-I mixture distribution and Gaussian mixture distribution. The results indicated that the data we generated are multimodally detected. Based on the KLD, the EV-I mixture distribution has better coverage than the Gaussian mixture distribution.  
We also implemented our algorithm for real datasets, namely enzyme, acidity, and galaxy datasets. We compared the EV-I mixture distribution with the Gaussian mixture distribution for all three datasets. Based on the KLD, we found that the EV-I mixture distribution has better coverage than the Gaussian mixture distribution. Then, visually, the results also show that the EV-I mixture distribution has better coverage. We also compared the EV-I mixture distribution with the Gaussian mixture distribution for the DHF data in eastern Surabaya. In our previous research, we analyzed the data using the Weibull distribution. We do not know whether the data were identified as multimodal or not. Using our algorithm, we found that the data are multimodal with four components. We also compared the EV-I mixture distribution and the Gaussian mixture distribution. Again, the EV-I mixture distribution indicated better coverage, seen both through the KLD and visually.  
Figure 11: Comparison of predictive densities for DHF data in eastern Surabaya using the four-component EV-I mixture distribution and the four-component Gaussian mixture distribution.  
**Author Contributions:** [PERSON], N.I. and I. designed the research; [PERSON] collected and analyzed the data and drafted the paper. All authors have critically read and revised the draft and approved the final paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Ministry of Education, Culture, Research, and Technology Indonesia, which gave the scholarship in Program Magister Menuju Doktor Untuk Sarjana Unggul (PMDSU).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Acknowledgments:** The authors thank the referees for their helpful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.