# Mixture weights optimisation for Alpha-Divergence Variational Inference

@inproceedings{Daudel2021MixtureWO, title={Mixture weights optimisation for Alpha-Divergence Variational Inference}, author={Kam'elia Daudel and Randal Douc}, booktitle={NeurIPS}, year={2021} }

This paper focuses on α -divergence minimisation methods for Variational Inference. We consider the case where the posterior density is approximated by a mixture model and we investigate algorithms optimising the mixture weights of this mixture model by α -divergence minimisation, without any information on the underlying distribution of its mixture components parameters. The Power Descent, defined for all α ̸ = 1 , is one such algorithm and we establish in our work the full proof of its…

## One Citation

Variational inference via Wasserstein gradient flows

- Computer ScienceArXiv
- 2022

This work proposes principled methods for VI, in which π̂ is taken to be a Gaussian or a mixture of Gaussians, which rest upon the theory of gradient flows on the Bures–Wasserstein space of Gaussian measures.

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