Variational Inference
A core method for approximate Bayesian inference that reformulates posterior computation as an optimization problem, approximating the true posterior with a parameterized distribution by maximizing the evidence lower bound (ELBO). Mean-field variational inference, which assumes independence among parameters, is the most commonly used approximation strategy.
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This study proposes a scalable variational inference framework for inference over non-Gaussian spatial fields.
In Bayesian neural networks with continuously increasing width M, the KL divergence regularization term in the ELBO gradually overwhelms the expected log-likelihood term, causing the variational predictive distribution to degenerate into the prior predictive distribution.
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This study proposes a scalable variational inference framework for inference over non-Gaussian spatial fields.
50%UnverifiedIn Bayesian neural networks with continuously increasing width M, the KL divergence regularization term in the ELBO gradually overwhelms the expected log-likelihood term, causing the variational predictive distribution to degenerate into the prior predictive distribution.
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