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

Peter Green (1) , Krzysztof Latuszyski (2) , Marcelo Pereyra (1) , Christian Robert (3, 4, 2, 5)

1 University of Bristol [Bristol]
2 University of Warwick [Coventry]
3 IUF - Institut universitaire de France
4 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
5 CREST - Centre de Recherche en Économie et Statistique

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.

Mots clés: ABC methods; Bayesian optimisation; MCMC ALGORITHMS

Domaines: Modélisation et simulation

Peter Green, Krzysztof Latuszyski, Marcelo Pereyra, Christian Robert. Bayesian computation: a perspective on the current state, and sampling backwards and forwards. 2015. ⟨hal-01113421⟩