Bayesian computation: a summary of the current state, and samples backwards and forwards

By Peter J. Green, K. Latuszy'nski, Marcelo Pereyra, Christian P. Robert. 2015

197 citations recorded by the source corpus.

Recent decades have seen enormous improvements in computational inference for statistical models; there have been competitive continual enhancements in a wide range of computational tools. In Bayesian inference, first and foremost, MCMC techniques have continued to evolve, moving from random walk proposals to Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical and algorithmic innovations opening new opportunities to practitioners. However, this impressive evolution in capacity is confronted by an even steeper increase in the complexity of the datasets to be addressed. The difficulties of modelling and then handling ever more complex datasets most likely call for a new type of tool for computational inference that dramatically reduces the dimension and size of the raw data while capturing its essential aspects. Approximate models and algorithms may thus be at the core of the next computational revolution.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

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Alain Oliviero Durmus

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Citations in the corpus

Approximating Bayesian confidence regions in convex inverse problems (2016, unknown): “The approximations can be computed very efficiently by using standard convex optimisation techniques [3], and are available as a free by-product in any problem that is solved by maximum-a-posteriori (MAP) estimation.”
Geometric ergodicity of the Bouncy Particle Sampler (2018, background): “in computational statistics [20], machine learning [1], molecular dynamics [6].”