andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1710 knowledge-graph by maker-knowledge-mining
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Introduction: We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for 1.1.0 have been fixed. We’ve also added a bit more functionality. The substantial changes are listed in the following release notes. v1.1.1 (5 February 2012) ====================================================================== Bug Fixes ———————————- * fixed bug in comparison operators, which swapped operator< with operator<= and swapped operator> with operator>= semantics * auto-initialize all variables to prevent segfaults * atan2 gradient propagation fixed * fixed off-by-one in NUTS treedepth bound so NUTS goes at most to specified tree depth rather than specified depth + 1 * various compiler compatibility and minor consistency issues * f
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1 org/ This is a patch release and is fully backward compatible with Stan and RStan 1. [sent-6, score-0.232]
2 The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for 1. [sent-9, score-0.457]
3 The substantial changes are listed in the following release notes. [sent-13, score-0.088]
4 ) * replace boost sign() to avoid compiler conflicts * trapping mismatched dimension assignments in arrays, matrices, and vectors Enhancements ———————————- * user’s guide chapters w. [sent-17, score-0.403]
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Introduction: Marc Tanguay writes in with a specific question that has a very general answer. First, the question: I [Tanguay] am currently running a MCMC for which I have 3 parameters that are restricted to a specific space. 2 are bounded between 0 and 1 while the third is binary and updated by a Beta-Binomial. Since my priors are also bounded, I notice that, conditional on All the rest (which covers both data and other parameters), the density was not varying a lot within the space of the parameters. As a result, the acceptance rate is high, about 85%, and this despite the fact that all the parameter’s space is explore. Since in your book, the optimal acceptance rates prescribed are lower that 50% (in case of multiple parameters), do you think I should worry about getting 85%. Or is this normal given the restrictions on the parameters? First off: Yes, my guess is that you should be taking bigger jumps. 85% seems like too high an acceptance rate for Metropolis jumping. More generally, t
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