andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1825 knowledge-graph by maker-knowledge-mining
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Introduction: Zhiqiang Tan writes: I have created an R package to implement the full likelihood method in Kong et al. (2003). The method can be seen as a binless extension of so-called Weighted Histogram Analysis Method (UWHAM) widely used in physics and chemistry. The method has also been introduced to the physics literature and called the Multivariate Bennet Acceptance Ratio (MBAR) method. But a key point of my implementation is to compute the free energy estimates by minimizing a convex function, instead of solving nonlinear equations by the self-consistency or the Newton-Raphson algorithm.
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1 Zhiqiang Tan writes: I have created an R package to implement the full likelihood method in Kong et al. [sent-1, score-1.133]
2 The method can be seen as a binless extension of so-called Weighted Histogram Analysis Method (UWHAM) widely used in physics and chemistry. [sent-3, score-1.113]
3 The method has also been introduced to the physics literature and called the Multivariate Bennet Acceptance Ratio (MBAR) method. [sent-4, score-1.015]
4 But a key point of my implementation is to compute the free energy estimates by minimizing a convex function, instead of solving nonlinear equations by the self-consistency or the Newton-Raphson algorithm. [sent-5, score-1.81]
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Introduction: Zhiqiang Tan writes: I have created an R package to implement the full likelihood method in Kong et al. (2003). The method can be seen as a binless extension of so-called Weighted Histogram Analysis Method (UWHAM) widely used in physics and chemistry. The method has also been introduced to the physics literature and called the Multivariate Bennet Acceptance Ratio (MBAR) method. But a key point of my implementation is to compute the free energy estimates by minimizing a convex function, instead of solving nonlinear equations by the self-consistency or the Newton-Raphson algorithm.
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Introduction: Tomas Iesmantas writes: I’m dealing with high dimensional (40-50 parameters) hierarchical bayesian model applied to nonlinear Poisson regression problem. Now I’m using an adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift (Yves F. Atchade, 2003) to obtain samples from posterior. But this algorithm is not very efficient in my case, it needs several millions iterations as burn-in period. And simulation takes quite a long time, since algorithm has to work with 40×40 matrices. Maybe you know another MCMC algorithm which could take not so many burn-in samples and would be able to deal with nonlinear regression? In non-hierarchical nonlinear regression model adaptive metropolis algorithm is enough, but in hierarchical case I could use something more effective. My reply: Try fitting the model in Stan. If that doesn’t work, let me know.
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Introduction: Malka Gorfine writes: We noticed that the important topic of association measures and tests came up again in your blog, and we have few comments in this regard. It is useful to distinguish between the univariate and multivariate methods. A consistent multivariate method can recognise dependence between two vectors of random variables, while a univariate method can only loop over pairs of components and check for dependency between them. There are very few consistent multivariate methods. To the best of our knowledge there are three practical methods: 1) HSIC by Gretton et al. (http://www.gatsby.ucl.ac.uk/~gretton/papers/GreBouSmoSch05.pdf) 2) dcov by Szekely et al. (http://projecteuclid.org/euclid.aoas/1267453933) 3) A method we introduced in Heller et al (Biometrika, 2013, 503—510, http://biomet.oxfordjournals.org/content/early/2012/12/04/biomet.ass070.full.pdf+html, and an R package, HHG, is available as well http://cran.r-project.org/web/packages/HHG/index.html). A
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