mcmc toolbox Search Results


statistics toolbox  (MathWorks Inc)
96
MathWorks Inc statistics toolbox
Statistics Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mcmc+toolbox/pmc05167349__mmc2-236-50-53?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
statistics toolbox - by Bioz Stars, 2026-07
96/100 stars
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population mcmc sampling parallel computing toolbox  (MathWorks Inc)
95
MathWorks Inc population mcmc sampling parallel computing toolbox
Efficiency of the <t>MCMC</t> methods. (A) Predicted voltage using the posterior mean computed from 1400 samples based on random walk Metropolis–Hastings algorithm. (B) Same as A but with the slice-sampling algorithm. (C) Same as A but with adaptive Metropolis algorithm based on stochastic approximations. (D) Same as A but with population Metropolis algorithm based on proposal exchange. (E) Schematic displaying (effective) samples drawn from the posterior density using the MH algorithm. Parameters 1 and 10 are plotted. (F) Same as E but using the slice-sampling algorithm. (G) Same as E but using the adaptive Metropolis algorithm. (H) Same as E but using the population Metropolis algorithm.
Population Mcmc Sampling Parallel Computing Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mcmc+toolbox/pmc04410946-49-1-8?v=MathWorks+Inc
Average 95 stars, based on 1 article reviews
population mcmc sampling parallel computing toolbox - by Bioz Stars, 2026-07
95/100 stars
  Buy from Supplier

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Efficiency of the MCMC methods. (A) Predicted voltage using the posterior mean computed from 1400 samples based on random walk Metropolis–Hastings algorithm. (B) Same as A but with the slice-sampling algorithm. (C) Same as A but with adaptive Metropolis algorithm based on stochastic approximations. (D) Same as A but with population Metropolis algorithm based on proposal exchange. (E) Schematic displaying (effective) samples drawn from the posterior density using the MH algorithm. Parameters 1 and 10 are plotted. (F) Same as E but using the slice-sampling algorithm. (G) Same as E but using the adaptive Metropolis algorithm. (H) Same as E but using the population Metropolis algorithm.

Journal: Neuroimage

Article Title: Gradient-free MCMC methods for dynamic causal modelling

doi: 10.1016/j.neuroimage.2015.03.008

Figure Lengend Snippet: Efficiency of the MCMC methods. (A) Predicted voltage using the posterior mean computed from 1400 samples based on random walk Metropolis–Hastings algorithm. (B) Same as A but with the slice-sampling algorithm. (C) Same as A but with adaptive Metropolis algorithm based on stochastic approximations. (D) Same as A but with population Metropolis algorithm based on proposal exchange. (E) Schematic displaying (effective) samples drawn from the posterior density using the MH algorithm. Parameters 1 and 10 are plotted. (F) Same as E but using the slice-sampling algorithm. (G) Same as E but using the adaptive Metropolis algorithm. (H) Same as E but using the population Metropolis algorithm.

Article Snippet: For population MCMC sampling Parallel Computing Toolbox (The MathWorks Inc., USA) was used.

Techniques: Sampling