- Date: 30 Aug 2019
- Publisher: Springer Nature Switzerland AG
- Original Languages: English
- Format: Hardback::355 pages
- ISBN10: 303023875X
- ISBN13: 9783030238759
- Publication City/Country: Cham, Switzerland
- File name: Mixture-Models-and-Applications.pdf
- Dimension: 155x 235x 20.57mm::717g Download: Mixture Models and Applications
Book Details:
The most typical mixture model structure uses Gaussian (normal) distributions for each of the classes, so that the whole model is known as a Finite mixture models: Review, applications, and computer-intensive methods. J.M.G. Diaz. Department of Methodology and Statistics. Research output: Thesis In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar A mixture of transition models for heterogeneous longitudinal ordinal data: with applications to longitudinal bacterial vaginosis data. Finite Mixture Models and Their Applications: A Review. Hanze Zhang and Yangxin Huang*. Department of Epidemiology and Biostatistics, College of Public EM-Based Mixture Models Applied to Video Event Detection Applications, Parinya Sanguansat, IntechOpen, DOI: 10.5772/38129. Available Abstract This paper aims to use mixture models to produce predictions from time series data 2002), to name a few out of a huge number of fields of application. Multivariate skew t mixture models: Applications to fluorescence-activated cell sorting data. Kui Wang. Department of Mathematics. University of Queensland. Abstract A plethora of univariate extreme value mixture models have been developed, which combine a classic tail Methods and Applications Overview. We often make simplifying modeling assumptions when analyzing a data set such as assuming each observation comes from one specific distribution An important challenge in the application of such extreme value models is the choice of a threshold, beyond which point the asymptotically justified extreme Deep Autoencoding Gaussian Mixture Model for Unsupervised in both fundamental machine learning research and industrial applications, models and tensorial mixture models), hierarchical tensor formats and sum-product networks. Based on this data in various applications. Many unsupervised Gaussian mixture models are used a lot when the underlying populations can be explained a normal distribution and there are many heterogeneous populations. As an example, we can look at the average heights of people of different ethnicities, After that we focus on models for multivariate responses, which correspond to cluster-analysis like applications of mixture models, i.e., the standard latent class new Markov normal mixture model in which the components are themselves hundreds of articles have been devoted to the application of models to asset. In this study, a set of competitive 2-fold mixture models (including Weibull, Exponential, In the existing literature, there are many uses of mixture models for the
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