simultaneously, simple use of significance tests can easily lead to false positive findings. We propose a sequential procedure for estimating the empirical null distribution of multiple hypothesis testing and apply the procedure to identify differentially expressed genes in microarray experiments. Our procedure

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates only the first order moment instead of the full multi-target posterior. Recently, a sequential Monte Carlo (SMC) implementation of PHD

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between

-tuning” of pectoral and then pelvic locomotor systems after the origin of flight (“pectoral early–pelvic late ” hypothesis). We use one of the most inclusive phylogenetic data sets of basal birds to investigate properties of this method and to consider the application of a Bayesian phylogenetic approach. Bayes factor

1 Workshop Context Ecosystems are dynamic in both space and time, hence involve multiple spatial and temporal scales, and are often heterogeneous in both of those dimensions, leading to spatial and temporal clustering. Accommodating this complexity in the context of scientific (statistical

for pre-registration, ways to prevent experimenter bias, and a comparison between Bayesian approaches and null-hypothesis significance testing (NHST) are discussed. Sequential analyses, which are widely used in large-scale medical trials, provide an efficient way to perform high-powered informative

-based, classical hypothesis-based, Bayesian inference, fuzzy inference, neural networks, etc. in this work, we introduce a new data fusion model that contributes to the area of multi-senor/source data fusion. The new fusion model relies on image processing theory to map stimuli from sensors onto an energy map

method of iterative hypothesis testing to perform rock typing by simultaneously honoring different logging-tool physics in a multi-layered earth model. In addition to estimating the vertical distribution of rock types with maximum likelihood, the Bayesian method quantifies the uncertainty of rock types

with no covariate adjustment and no multiple comparisons adjustment) to a Bayesian logistic regression model (with adjusted covariates chosen using data mining). However, there are no methods that mathematically are "in between," and what methods are available are not simpler to perform. In sequence

Abstract—An important receiver operation is to detect the presence specific preamble signals with unknown delays in the presence of scattering, Doppler effects and carrier offsets. This task, referred to as “link acquisition”, is typically a sequential search over the transmitted signal space