Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent ...
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NeurIPS workshop on Advances in Approximate Bayesian Inference - approximateinference/approximateinference.github.io
Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent ...
Another example of using a dropout approach to approximate Bayesian inference in deep Gaussian processes is the work of Gal and Ghahramani292. Deep ensemble methodologies293–296 combine deep learning modelling with ensemble learning. Ensemble methods utilize multiple models and different random ...
an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for...
内容提示: 1Advances in Variational InferenceCheng Zhang Judith Bütepage Hedvig Kjellström Stephan MandtAbstract—Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesianprobabilistic models. These models are usually intractable and thus require approximate inference. ...
(2020) into a unifying Bayesian formalism, which allows to develop approximations and alternative algorithms. In the works described above and, to the Authors' knowledge, in other recent relevant literature in the geophysical domain, the application of ML techniques for model error inference and ...
In addition to FQE and Soft OPC, other OPE methods offer valuable alternatives for assessing automated anesthesia strategies. These methods include weighted importance sampling, approximate model, and weighted doubly robust estimator, etc. Combining these approaches can enhance the robustness of policy eva...
Fine mapping causal variants with an approximate bayesian method using marginal test statistics. Genetics 200, 719–736 (2015). Article PubMed PubMed Central CAS Google Scholar Benner, C. et al. FINEMAP: Efficient variable selection using summary data from genome-wide association studies. ...