Results 41 to 50 of about 5,051,769 (292)
CoRR, 2021 We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate densities, which can serve as ...
Esmaeili, B. +3 more
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Variational Inference for Logical Inference
CoRR, 2017 Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contributions to this framework. The first is to show how a type of logical inference can be performed by evaluating conditional probabilities.
Emerson, Guy, Copestake, Ann
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Wasserstein Variational Inference
CoRR, 2018 This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases.
Ambrogioni, L. +5 more
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fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets
Genetics, 2014 Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here, we develop
Anil Raj, M. Stephens, J. Pritchard
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Implicit Copula Variational Inference
Journal of Computational and Graphical Statistics, 2022 Key to effective generic, or "black-box", variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promising options, but calibration of the approximation can be slow for some choices. Smith et al.
Michael Stanley Smith, Ruben Loaiza-Maya
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Memorized Variational Continual Learning for Dirichlet Process Mixtures
IEEE Access, 2019 Bayesian nonparametric models are theoretically suitable for streaming data due to their ability to adapt model complexity with the observed data. However, very limited work has addressed posterior inference in a streaming fashion, and most of the ...
Yang Yang, Bo Chen, Hongwei Liu
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Operator Variational Inference
CoRR, 2016 Appears in Neural Information Processing Systems ...
Rajesh Ranganath +3 more
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Variationally Inferred Sampling through a Refined Bound
Entropy, 2021 In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation.
Víctor Gallego, David Ríos Insua
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Proximity Variational Inference
CoRR, 2017 Variational inference is a powerful approach for approximate posterior inference. However, it is sensitive to initialization and can be subject to poor local optima. In this paper, we develop proximity variational inference (PVI). PVI is a new method for optimizing the variational objective that constrains subsequent iterates of the variational ...
Jaan Altosaar +2 more
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Boosting Variational Inference
CoRR, 2016 Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Fangjian Guo +4 more
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