Investigating Complex Isochron Data Using Mixture Models
نویسندگان
چکیده
منابع مشابه
Investigating population heterogeneity with factor mixture models.
Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common fac...
متن کاملMixture models for assessing differential expression in complex tissues using microarray data
MOTIVATION The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression d...
متن کاملModel Selection for Mixture Models Using Perfect Sample
We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixt...
متن کاملCure Fraction Models Using Mixture and Non-mixture Models
We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Twomodels are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a lifetime data set.
متن کاملPositive Data Clustering Using Finite Inverted Dirichlet Mixture Models
Positive Data Clustering Using Finite Inverted Dirichlet Mixture Models Taoufik BDIRI In this thesis we present an unsupervised algorithm for learning finite mixture models from multivariate positive data. Indeed, this kind of data appears naturally in many applications, yet it has not been adequately addressed in the past. This mixture model is based on the inverted Dirichlet distribution, whi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geochemistry, Geophysics, Geosystems
سال: 2018
ISSN: 1525-2027
DOI: 10.1029/2018gc007548