Bayesian hierarchical model
Bayesian hierarchical model for the prediction of football results
... a Bayesian hierarchical model to address both these aims and test its predic- tive strength on data about the Italian Serie A championship ...
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A composite Bayesian hierarchical model of compositional data with zeros
... set was partitioned according to the presence or absence of the elements iron and potassium, and a Bayesian hierarchical model was fit to each resulting subset of the data. While this approach allows ...
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A composite Bayesian hierarchical model of compositional data with zeros
... set was partitioned according to the presence or absence of the elements iron and potassium, and a Bayesian hierarchical model was fit to each resulting subset of the data. While this approach allows ...
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Smoothing and Mean–Covariance Estimation of Functional Data with a Bayesian Hierarchical Model
... We contrast our proposed approach with existing GP methods for exploiting a Bayesian hierarchical model with a GP prior for the mean curve and an Inverse-Wishart process (IWP) prior for the ...
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Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder
... 8 ANALYSIS OF OBSERVATIONS 8.1 Outlier-clipped data We examine the three-anchor, outlier-clipped R16 dataset using three variants of the BHM. In the first case, we use our vanilla version, sampling anchor likelihoods ...
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A Bayesian Hierarchical Model For Longitudinal Data
... INTRODUCTION Bayesian methods are based on the assumption that probability is operationalized as a degree of belief, and not a frequency as is done in classical, or frequentist, ...
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A Bayesian hierarchical model for comparing average F1 scores
... probabilistic model for comparing the average F 1 scores has been described in the multi-class single-label (aka “one-of”) classification setting, but it is readily extensible to the multi-class multi-label (aka ...
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A Bayesian Hierarchical Model for Learning Natural Scene Categories
... Each categories of scenes was split randomly into two separate sets of images, N (100) for training and the rest for testing. A codebook of codewords was learnt from patches drawn from a random half of the entire ...
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A Bayesian hierarchical model for comparing average F1 scores
... This approach largely avoids the above mentioned perils of NHST, except for the third one on complex performance measures. However, it is known that the value of Bayes factor can be very sensitive to the choice of prior ...
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A Bayesian Hierarchical Model for Estimating Nutrient Export Rates in a Developing Watershed.
... The model developed here includes changes in precipitation, land-use, point-source discharge, and livestock operations to capture temporal variability in nitrogen ...A Bayesian-hierarchical approach ...
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Point source moment tensor inversion through a Bayesian hierarchical model
... Characterization of seismic sources is an important aspect of seismology. Parameter uncer- tainties in such inversions are essential for estimating solution robustness, but are rarely available. We have developed a ...
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Likelihood-Free Inference of Population Structure and Local Adaptation in a Bayesian Hierarchical Model
... some model of mutation and ...by Bayesian hierarchical models, in which parameters, such as mutation rates and selection coefficients, are allowed to vary across ...approximate Bayesian ...
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A full Bayesian hierarchical mixture model for the variance of gene differential expression
... ture model for gene expression ...a Bayesian hierarchical model, we are able to model various sources of variability in a common model, thus propagat- ing ...a Bayesian ...
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Hierarchical bayesian modeling of pharmacophores in bioinformatics
... pharmacophore model characterises the physico-chemical properties com- mon to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial ...a Bayesian ...
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Bayesian Hierarchical Models for Model Choice
... We first show the bivariate contour plots of the negative logarithm of prior den- sities of independent double exponentials and independent normals in the two upper panels of Figure 3.1. Between ridge regression and the ...
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An Infinite Hierarchical Bayesian Model of Phrasal Translation
... that the probability of its two subtrees are inter- dependent. This is best understood in terms of the Chinese Restaurant Franchise (CRF; Teh et al. (2006)), which describes the posterior distribution after integrating ...
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A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge
... This paper follows a series of more recent work which aims to infer script knowledge automati- cally from data. Chambers and Jurafsky (2008) present a system which learns narrative chains from newswire texts. Relevant ...
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An Improved Hierarchical Bayesian Model of Language for Document Classification
... approximate model which overcomes some of these concerns, and demonstrate substantial improvements that such a model achieves on four classification tasks, three of which are standard and one of which is a ...
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A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences
... Although in the motif detection setting the HMDM model involves a complex missing data problem in which both the output and the internal states of the HMDM are hidden, we show that a var[r] ...
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A Hierarchical Bayesian Model for Next-Generation Population Genomics
... alternative model prior would not provide posterior estimates of a ST or b ST , which are necessary to specify the posterior distribution for the genome-level distribution of f ST , as opposed to the posterior ...
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