کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6024996 1580895 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Gradient-free MCMC methods for dynamic causal modelling
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
Gradient-free MCMC methods for dynamic causal modelling
چکیده انگلیسی
In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density - albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 112, 15 May 2015, Pages 375-381
نویسندگان
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