The ability of predicting growth was compared for the different models through predictive cross-validation using predictive log-scores of the models [ 17], approximated by evaluating the models based on posterior mean parameter estimates.
However, it depends on prior input, and this dependence does not decrease as sample size increases, unlike parameter's estimation based on posterior distributions [ 20].
Parameter estimates were based on posterior probability distributions constructed by sampling the stationary distribution for 40,000,000 generations, sampling every 1000 steps.
Then, inferences about parameter values are based on posterior probabilities, which are obtained using Bayes theorem as (16) where f y | θ ) is the conditional density of the data vector y given the vector of parameter values θ, and f is the prior probability density of θ.
To further understand differences between s- and i-proteasome and how substrate transport- and hydrolysis steps affect overall substrate degradation, we perform sensitivity analysis based on the posterior parameter distribution.
Post hoc fits are overlay plots of predicted (i.e., based on the posterior mean parameter estimates for each individual) and observed dependent variable vs. time, allowing users to assess whether the model fits match the observations for each individual.
Estimates of the parameters were based on the posterior distribution of those parameters, p (T, M, θ | X), where T is the time of split, M is the migration rate that may be asymmetrical (that is m1 ≠ m2), θ is the population parameter 2Neμ, and X is the data.
The full Bayesian framework for BGS-NMF model based on the posterior distribution of parameters and hyperparameters p Θ,Φ|X) is not analytically tractable.
Based on the parameter estimates and the associated covariance matrix, the posterior predictive distribution of the parameters can be constructed.
