Indeed, both frequentist and Bayesian inference can entail various drawbacks, due to the complexity or misspecification of the model, or to the presence of many nuisance parameters. These difficulties may be overcome from a theoretical point of view through approximate likelihoods and scoring rules, ...
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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 ...
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 ...
Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion). J. R. Stat. Soc. B 71, 319–392 (2009) Article MathSciNet MATH Google Scholar Schlather, M.: On the second-order characteristi...
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...
5 Outliers: A Path in Research. . . . . . . . . . . . . . . . . . . . . . ...
Sun S (2013) A review of deterministic approximate inference techniques for Bayesian machine learning. Neural Comput Appl 23:2039–2050. https://doi.org/10.1007/s00521-013-1445-4 Article Google Scholar Pedro HTC, Coimbra CFM, David M, Lauret P (2018) Assessment of machine learning techniques...
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. ...