Bayesian parameter estimation and model comparison | The BMC approach uses the table of model evidence values (subjects x models; see S2 Table) to estimate a posterior distribution over r,-. |
Bayesian parameter estimation and model comparison | The mean of this posterior distribution , mp(i), is our best estimate of ri. |
Bayesian parameter estimation and model comparison | Given a table of model evidence values (see S2 Table) an algorithm [101,102] can be derived for computing a posterior distribution over ri, from which subsequent inferences can be made. |
Exerted force (% MVC) n | The resulting exceedance probability (xp) indicates the likelihood of each model to be the most frequently occurring model in the population, and the mean of the posterior distribution (mp) provides an estimate of the frequency with which the model appears in the population. |
Interface Identification and Clustering | We defined WC 2 exp(aC), used noninformative normal priors (with zero mean and large standard deviation) for the parameters ac, and employed Gibbs sampling to obtain the posterior distributions of pC, for which we report the average as well as (2.5%,97.5%) confidence intervals. |
Interface Identification and Clustering | Again, we defined the parameters 190- so that the Poisson intensity is (t kon ci) 2 exp(bCi), where 190- have non-infor-mative normal priors, and sampled the posterior distribution of kon using Gibbs sampling. |
Interface Identification and Clustering | For all estimates, 104 samples from the posterior distributions were obtained after a 5><103 burn-in phase using Markov-chain Monte Carlo techniques [44]. |
Position-Dependent Diffusion Coefficients | The diffusion coefficient D was calculated by sampling the posterior distribution of the rate matriX from the posterior distribution p(K|X) = p(X|K) p(K), assuming a uniform prior p(K), and the likelihood |