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Bayesian computation: a summary of the current state, and samples backwards and forwards
2015
Statistics and computing
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
doi:10.1007/s11222-015-9574-5
fatcat:mdlw3fdtvjfkxjyo2ivgwdv4oe