Bayesian computation: a perspective on the current state, and sampling backwards and forwards

Green, Peter; Latuszyski, Krzysztof; Pereyra, Marcelo; Robert, Christian P.

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 publié

External document link

https://arxiv.org/abs/1502.01148v3

Date

2015

Journal name

Statistics and Computing

Volume

Number

Publisher

Chapman & Hall

Published in

Paris

Pages

835-862

Abstract (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 algorithms

JEL

C11 - Bayesian Analysis: General