%0 Journal Article %1 roberts2009examples %A Roberts, Gareth O %A Rosenthal, Jeffrey S %D 2009 %I Taylor & Francis %J Journal of Computational and Graphical Statistics %K MCMC adaptive_MCMC computation mixing_time %N 2 %P 349--367 %T Examples of adaptive MCMC %U http://www.utstat.utoronto.ca/WSFiles/technicalreports/0610.pdf %V 18 %X We investigate the use of adaptive MCMC algorithms to auto- matically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario et al. (2001), Metropolis-within-Gibbs algorithms for non-conjugate hierarchical models, re- gionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algorithms perform very well compared to non- adaptive algorithms, even in high dimension.

@article{roberts2009examples, abstract = {We investigate the use of adaptive MCMC algorithms to auto- matically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario et al. (2001), Metropolis-within-Gibbs algorithms for non-conjugate hierarchical models, re- gionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algorithms perform very well compared to non- adaptive algorithms, even in high dimension.}, added-at = {2014-03-05T19:37:46.000+0100}, author = {Roberts, Gareth O and Rosenthal, Jeffrey S}, biburl = {https://www.bibsonomy.org/bibtex/2948ca54112c4828ae02ba15aa45ae772/peter.ralph}, interhash = {b844071fe8aa0cc52f83947e899df081}, intrahash = {948ca54112c4828ae02ba15aa45ae772}, journal = {Journal of Computational and Graphical Statistics}, keywords = {MCMC adaptive_MCMC computation mixing_time}, number = 2, pages = {349--367}, publisher = {Taylor \& Francis}, timestamp = {2014-03-16T10:55:49.000+0100}, title = {Examples of adaptive MCMC}, url = {http://www.utstat.utoronto.ca/WSFiles/technicalreports/0610.pdf}, volume = 18, year = 2009 }