doi abstract bibtex

This work extends Bayesian regression as an adaptive that augmented by deep neural networks (the probabilistic encoder) to obtain the posterior probability distributions of the regression coefficients. We use variational inference to obtain the conditional distribution over the regression coefficients, which are the latent space given the observed data. Therefore, our model can recognize local conditional probability distribution of the regression coefficients for each observation as well as it can measure the uncertainty of the model locally. Experimental results on the benchmark datasets prove that our Bayesian Meta Regression (BMR) significantly outperforms baseline regression techniques and precisely measures the model uncertainty for each observation.

@article{Pham2021, title = {Bayesian {Meta} {Regression}}, volume = {212}, issn = {21903026}, doi = {10.1007/978-981-33-6757-9_7}, abstract = {This work extends Bayesian regression as an adaptive that augmented by deep neural networks (the probabilistic encoder) to obtain the posterior probability distributions of the regression coefficients. We use variational inference to obtain the conditional distribution over the regression coefficients, which are the latent space given the observed data. Therefore, our model can recognize local conditional probability distribution of the regression coefficients for each observation as well as it can measure the uncertainty of the model locally. Experimental results on the benchmark datasets prove that our Bayesian Meta Regression (BMR) significantly outperforms baseline regression techniques and precisely measures the model uncertainty for each observation.}, journal = {Smart Innovation, Systems and Technologies}, author = {Munkhdalai, Lkhagvadorj and Pham, Van Huy and Ryu, Keun Ho}, year = {2021}, note = {ISBN: 9789813367562}, keywords = {Bayesian regression, Variational autoencoder, Variational inference}, pages = {52--59}, }

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