This paper describes an approach to Bayesian sensitivity analysis that uses an innuence statistic and an outlier statistic to assess the sensitivity of a model to perturbations. The basic outlier statistic is a Bayes factor, while the innuence statistic depends strongly on the purpose of the analysis. The task of innuence analysis is aided by having an interpretable innuence statistic. Two alte...

We compare the diagnostic accuracy of three diagnostic inference models: the simple Bayes model, the multimembership Bayes model, which is isomorphic to the parallel combination function in the certainty-factor model, and a model that incorporates the noisy OR-gate interaction. The comparison is done on 20 clinicopathological conference (CPC) cases from the American Journal of Medicine—challeng...

Bayes factors have been advocated as superior to p-values for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of p-values, inference by p-values is still nearly ubiquitous. One impediment to the adoption of Bayes factors is a lack of practical development, particularly a lack of ready-to-use formulas and algorithms. In this paper, we discuss and...

One formulation of forensic identification source problems is to determine the trace evidence, for instance, glass fragments found on a suspect crime. The current state science compute Bayes factor comparing marginal distribution measurements evidence under two competing propositions whether or not unknown originated from specific source. obvious problem with such an approach ability tailor pri...

We investigate Bayesian variable selection in models driven by Gaussian processes, which allows us to treat linear, nonlinear and nonparametric models, conjunction with even dependent setups, the same vein. consider Bayes factor route selection, develop a general asymptotic theory for process framework “large p, large n” settings $$p\gg n$$ , establishing almost sure exponential convergence of ...

In management research, empirical data are often analyzed using p-value null hypothesis significance testing (pNHST). Here we outline the conceptual and practical advantages of an alternative analysis method: Bayesian hypothesis testing and model selection using the Bayes factor. In contrast to pNHST, Bayes factors allow researchers to quantify evidence in favor of the null hypothesis. Also, Ba...