1Blurb:: 2Calculate model evidence (marginal likelihood of model) when using Bayesian methods 3 4Description:: 5Model evidence is used in Bayesian model selection and model averaging. 6It is defined as the probability of the data given the model, and 7is calculated by averaging the likelihood of the model parameters 8over all values of the model parameters according to their prior 9distributions. In Dakota, one must calculate the 10model evidence separately for each model and perform the normalization 11to obtain the posterior model plausibility for each model. 12 13<b> Default Behavior </b> 14 15When specifying \c model_evidence, there are two methods of 16calculating it. One or both may be specified. They 17include the Monte Carlo approximation, given by \c mc_approx 18and the Laplace approximation, given by \c laplace_approx. \c mc_approx 19is the default approach. 20 21<b> Expected Output </b> 22Currently, the model evidence will be printed in the screen output 23with prefacing text indicating if it is calculated by 24Monte Carlo sampling or the Laplace approximation. 25 26<b> Usage Tips </b> 27 28 29Topics:: 30 31Examples:: 32 33Theory:: 34Faq:: 35See_Also:: 36