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Bayesian estimation of mixtures with dynamic transitions and known component parameters. (English) Zbl 1227.93114
Summary: Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant component weights. Thus, mixtures with dynamic components and dynamic transitions between them are not supported. The present paper fills this gap. For the sake of simplicity and to give a better insight into the task, the paper considers mixtures with known components. A general case with unknown components will be presented soon.
MSC:
93E10 Estimation and detection in stochastic control theory
93E12 Identification in stochastic control theory
68T05 Learning and adaptive systems in artificial intelligence
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