Bayesian computation: a perspective on the current state, and sampling backwards and forwards
Green, Peter; Latuszyski, Krzysztof; Pereyra, Marcelo; Robert, Christian P. (2015), Bayesian computation: a perspective on the current state, and sampling backwards and forwards, Statistics and Computing, 25, 4, p. 835-862. 10.1007/s11222-015-9574-5
Type
Article accepté pour publication ou publiéExternal document link
https://arxiv.org/abs/1502.01148v3Date
2015Journal name
Statistics and ComputingVolume
25Number
4Publisher
Chapman & Hall
Published in
Paris
Pages
835-862
Publication identifier
Metadata
Show full item recordAbstract (EN)
The past decades have seen enormous im-provements in computational inference based on sta-tistical models, with continual enhancement in a wide range of computational tools, in competition. In Bayesian inference, first and foremost, MCMC techniques con-tinue to evolve, moving from random walk proposals to Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical and algorithmic inputs opening wider access to practitioners. However, this impressive evolution in capacity is confronted by an even steeper increase in the complexity of the models and 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 reduce 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.Subjects / Keywords
Bayesian analysis; optimisation; ABC techniques; MCMC algorithmsRelated items
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