We assess the use of an approximation to the Bayes factor for objectively assessing spatial segmentation models. The Bayes factor allows us to automatically determine thresholds, in multidimensional feature space, for such objectives as cloud mask definition. We compare our results with a cloud map currently provided as a data product.

Description R package for design of experiments for association studies for detection of linkage disequilibrium. Uses an existing deterministic power calculation for detection of linkage disequilibrium between a bi-allelic QTL and a bi-allelic marker, together with the Spiegelhalter and Smith Bayes factor to generate designs with power to detect effects with a given Bayes factor.

The empirical Bayes estimators of treatment effects in a factorial experiment were derived and their asymptotic properties were explored. It was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. A Bootstrap analysis was performed to ill...

In recent years, statisticians and psychologists have provided the critique that p-values do not capture the evidence afforded by data and are, consequently, ill suited for analysis in scientific endeavors. The issue is particular salient in the assessment of the recent evidence provided for ESP by Bem (2011) in the mainstream Journal of Personality and Social Psychology. Wagenmakers, Wetzels, ...

When testing a point null hypothesis versus an alternative that is vaguely speci ed, a Bayesian test usually proceeds by putting a non-parametric prior on the alternative and then computing a Bayes factor based on the observations. This paper addresses the question of consistency, that is, whether the Bayes factor is correctly indicative of the null or the alternative as the sample size increas...

When testing a point null hypothesis versus an alternative that is vaguely speciied, a Bayesian test usually proceeds by putting a nonparametric prior on the alternative and then computing a Bayes factor based on the observations. This paper addresses the question of consistency, that is, whether the Bayes factor is correctly indicative of the null or the alternative as sample size increases. W...

Motivated by the problem of minefield detection, we investigate the problem of classifying mixtures of spatial point processes. In particular we are interested in testing the hypothesis that a given dataset was generated by a Poisson process versus a mixture of a Poisson process and a hard-core Strauss process. We propose testing this hypothesis by comparing the evidence for each model by using...

Let M be a finite or countable set of models (here we only deal with finite M but Bayes factors make sense for countable M). For each model m ∈ M we have the prior probability of the model pri(m). It does not matter if this prior on models is unnormalized. Each model m has a parameter space Θm and a prior g(θ | m), θ ∈ Θm The spaces Θm can and usually do have different dimensions. That’s the po...