I use this method to test whether alternative parameterizations and priors improve the behavior of the approximate-Bayesian model-choice approach to estimating shared divergence events.
The framework combines three domains of travel demand modelling, namely a rationalist mode choice approach, a socio-geographical approach including various spatial factors, and a socio-psychological approach, building on established behavioural theories [ 23, 26, 49].
Like other models based on a discrete choice approach, labor supply is treated as the outcome of agents' choices from a finite set of job opportunities.
A coalescent approach to migration model choice (Beerli and Palczewski 2010) was adapted in order to determine the possible routes of spread of P. ramorum.
We use a Bayesian inference approach for studying the effect of model choice on tree inference.
Case study 2 shows the impact of model choice on the distribution of the predicted dose (Css vs. MEC-driven approach).
Bayesian approaches offer a coherent perspective by formally accounting for uncertainty in model choice, effect estimation, and imputation uncertainty [Stephens and Balding, 2009].
Despite the fact that some considerations on model choice will seem extra-statistical, in the sense that they fall outside the scope of statistical treatment, statistics offers several methods for approaching the choice of statistical models.
A Bayesian approach would formulate this as a posterior probability that conditions on both the observed data and prior uncertainty in model choice.
Methods used in choice approaches are multinomial logit model, conditional logit model, maximum-likelihood, or weighted least square method population [31].
Our approach can be applied to model choices of risk reduction mechanisms such as extended warranties, demonstrations, and sampling.
