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What is Amortized Bayesian Mixture Models about?

This paper introduces an Amortized Bayesian Inference (ABI) framework specifically designed for estimating Bayesian mixture models, addressing challenges such as high-dimensional posterior inference and label switching. The proposed method utilizes generative neural networks for parameter inference and classification networks for mixture membership identification, enabling both independent and dependent mixture models. The framework is validated through synthetic and real-world datasets, demonstrating its e

Author

mn-dandoh1423

Language

EN