The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents the posterior ...

Here we propose an approach using parallel runs of MCMC, variational, or mode-based inference to hit as many modes or separated regions as possible and then ...

The posterior multimodality is thereby a blessing rather than a curse under model misspecification. 6. Examples. We demonstrate the benefit of stacking by a ...

Using parallel runs of MCMC, variational, or mode-based inference to hit as many modes or separated regions as possible, and then combining these using ...

Jan 1, 2022 · The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents ...

The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents the posterior ...

Jun 24, 2020 · When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms can have difficulty moving between ...

Under misspecified models, stacking can give better predictive performance than full Bayesian inference, hence the multimodality can be considered a blessing ...

The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents the posterior ...

[2022] Stacking for non-mixing Bayesian computations: The curse and blessing of multimodal posteriors. Journal of Machine Learning Research. Yuling Yao ...