Akaike's Bayesian information criterion (ABIC) has been widely used in geophysical inversion and beyond. However, little done to investigate its statistical aspects. We present an alternative derivation of the marginal distribution measurements, whose maximization directly leads invention ABIC by Akaike. show that is statistically estimate variance measurements prior maximizing measurements. Th...

We present a Bayesian multimodality non-rigid image registration method. Since the likelihood is unknown in the general multimodality setting, we use a density estimator as a drop in replacement to the true likelihood. The prior is a standard small deformation penalty on the displacement field. Since mutual information-based methods are in widespread use for multimodality registration, we attem...

This paper proposes a model selection criterion for classification problems. The criterion focuses on selecting models that are discriminant instead of models based on the Occam’s razor principle of parsimony between accurate modeling and complexity. The criterion, dubbed Discriminative Information Criterion (DIC), is applied to the optimization of Hidden Markov Model topology aimed at the reco...

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics...