In their article titled ‘‘Estimating species trees using approximate Bayesian computation’’ Fan and Kubatko present an algorithm called ST-ABC to sample the posterior distribution of species trees (Molecular Phylogenetics and Evolution 59: 354– 363). The authors claim that ST-ABC is an approximate Bayesian computation (ABC) algorithm. Here, I argue that one of the steps in their algorithm diffe...

BACKGROUND Pedicled flaps based on the posterior auricular artery have been used for small auricular and mastoid cavity defects. OBJECTIVE To precisely define the vascular anatomy and angiosome (cutaneous distribution) of the posterior auricular artery. METHODS A fresh cadaver model was used for 3 separate investigations, studying the posterior auricular artery. Intra-arterial ink injection...

Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the informat...

ISSN: 0162-1459 (Print) 1537-274X (Online) Journal homepage: http://www.tandfonline.com/loi/uasa20 A Note on the Influence of the Sample on the Posterior Distribution Ward Whitt To cite this article: Ward Whitt (1979) A Note on the Influence of the Sample on the Posterior Distribution, Journal of the American Statistical Association, 74:366a, 424-426, DOI: 10.1080/01621459.1979.10482530 To link...

This study explores the posterior predictive distributions obtained with various Bayesian inference methods for neural networks. The quality of the distributions is assessed both visually and quantitatively using Kullback–Leibler (KL) divergence, Kolmogorov–Smirnov (KS) distance and precision-recall scores. We perform the analysis using a synthetic dataset that allows for a more detailed examin...

This article describes a procedure for deening a posterior distribution on the value of a normalizing constant or ratio of normalizing constants using output from Monte Carlo simulation experiments. The resulting posterior distribution provides a simple diagnostic for assessing the adequacy of a simulation experiment for estimating these quantities, and is particularly useful in cases for which...

Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we sh...

Wepropose amixedmultinomial logit model, with themixing distribution assigned a general (nonparametric) stick-breaking prior.Wepresent aMarkov chainMonte Carlo (MCMC) algorithm to sample and estimate the posterior distribution of the model’s parameters. The algorithm relies on a Gibbs (slice) sampler that is useful for Bayesian nonparametric (infinite-dimensional) models. The model and algorith...

Sampling from the posterior distribution is a natural goal, as it gives us essentially the best possible information about the parameters given the data. We can also use this sampling to solve other related problems, such as inference, where the goal is to compute the probability of a future event Y given data from the past X . This can be reduced to computing the expectation of Pr[Y |Θ] with r...

We examine properties of the CAR1 model, which is commonly used to represent regional eeects in Bayesian analyses of mortality rates. We consider a Bayesian hierarchical linear mixed model where the xed eeects have a v ague prior such a s a constant prior and the random eeect follows a class of CAR1 models including those whose joint prior distribution of the regional eeects is improper. We giv...