Abstract
Lately, data mining tools have received significant attention because of their capability in modeling and analyzing collected data in various fields including medical research. With the growing availability of medical data, it is crucial to develop models that can discover hidden patterns in data and analyze them. Among various techniques, mixture models have been widely used for categorization problems in statistical modeling. In this paper, a Bayesian learning framework is proposed for multivariate Beta mixture model. Previous works have shown that multivariate Beta distribution can be considered as an alternative to Gaussian due to the flexibility of its shape and convincing performance. In particular, we use the Birth and Death Markov Chain Monte Carlo (MCMC) algorithm, which allows simultaneous parameters estimation and model selection. Experimental results on medical applications demonstrate the effectiveness of the proposed algorithm.
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Amirkhani, M., Manouchehri, N., Bouguila, N. (2021). Birth-Death MCMC Approach for Multivariate Beta Mixture Models in Medical Applications. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_25
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