Approximate computation of projection depths

Approximate Bayesian Computation

Just when you thought it was safe to go back into the water, I’m going to complicate things even further. The Nielsen-Wakely-Hey [5, 3, 4] approach is very flexible and very powerful, but even it doesn’t cover all possible scenarios. It allows for non-equilibrium scenarios in which the populations from which we sampled diverged from one another at different times, but suppose that we think our ...

Adaptive approximate Bayesian computation

Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.’s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine im...

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices amon...

Projection learning: alternative approaches to the computation of the projection

Computation n ! ral Projection an *

One kind of temporal reasoning is temporal projection-the computation of the consequences of a set of events. This problem is related to a number of other temporal reasoning tasks such as story understanding, planning, and plan validation. We show that one particular simple case of temporal projection on partially ordered events turns out to be harder than previously conjectured. However, given...