BITESIZE | Making Variational Inference Reliable: From ADVI to DADVI

Learning Bayesian Statistics

Today’s clip is from episode 147 of the podcast, with Martin Ingram.

Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation.

The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.

Get the full discussion here.

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Transcript

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