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Mixture-model adaptation for SMT [PDF]
Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07, 2007 We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation;
Foster, George, Kuhn, Roland
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Biology Open, 2022 Torpor is a state of controlled reduction of metabolic rate (M) in endotherms. Assigning measurements of M to torpor or euthermy can be challenging, especially when the difference between euthermic M and torpid M is small, in species defending a high ...
Nicolas J. Fasel +2 more
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Mixture model averaging for clustering [PDF]
Advances in Data Analysis and Classification, 2014 In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one. In such circumstances, selection of this best model is achieved using a model selection criterion, most often the Bayesian information criterion.
Yuhong Wei, Paul D. McNicholas
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Osteosarcoma (OS) and Ewing sarcoma (EWS) are the most common primary bone cancers in children, but acute thrombosis is poorly characterized in this population. Our study evaluated the rates of venous thromboembolism (VTE) and associated risk factors in pediatric patients with bone sarcomas treated over a 10‐year period encompassing
Sarah Kappa +8 more
wiley +1 more source
Mixture models for distance sampling detection functions
, 2015 Funding: EPSRC DTGWe present a new class of models for the detection function in distance sampling surveys of wildlife populations, based on finite mixtures of simple parametric key functions such as the half-normal. The models share many of the features
David L. Miller (167484) +8 more
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POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models
Journal of Statistical Software, 2015 The software POPS performs inference of population genetic structure using multilocus genotypic data. Based on a hierarchical Bayesian framework for latent regression models, POPS implements algorithms that improve estimation of individual admixture ...
Flora Jay +3 more
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BAMBI: An R Package for Fitting Bivariate Angular Mixture Models
Journal of Statistical Software, 2021 Statistical analyses of directional or angular data have applications in a variety of fields, such as geology, meteorology and bioinformatics. There is substantial literature on descriptive and inferential techniques for univariate angular data, with the
Saptarshi Chakraborty, Samuel W. K. Wong
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On Learning Mixture Models for Permutations [PDF]
Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015 In this paper we consider the problem of learning a mixture of permutations, where each component of the mixture is generated by a stochastic process. Learning permutation mixtures arises in practical settings when a set of items is ranked by different sub-populations and the rankings of users in a sub-population tend to agree with each other.
CHIERICHETTI, FLAVIO +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Children with acute lymphoblastic leukemia (ALL) are at risk of severe outcomes from SARS‐CoV‐2 (SCV2). In the post‐pandemic context, where most children have been infected with SCV2, there are limited data on whether vaccination remains beneficial in children with ALL.
Janna R. Shapiro +11 more
wiley +1 more source
, 2011 We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007).
Walker, S. +5 more
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