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Bayesian Mixture Models For Metagenomic Community Profiling
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Book Synopsis Bayesian Mixture Models for Metagenomic Community Profiling by : Sofia Morfopoulou
Download or read book Bayesian Mixture Models for Metagenomic Community Profiling written by Sofia Morfopoulou and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models for Metagenomic Community Profiling by : S. Morfopoulou
Download or read book Bayesian Mixture Models for Metagenomic Community Profiling written by S. Morfopoulou and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Modeling in Bioinformatics by : Dipak K. Dey
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by CRC Press. This book was released on 2010-09-03 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
Book Synopsis Finite Bayesian Mixture Models with Applications in Spatial Cluster Analysis and Bioinformatics by :
Download or read book Finite Bayesian Mixture Models with Applications in Spatial Cluster Analysis and Bioinformatics written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Finite Bayesian Mixture Models with Applications in Spatial Cluster Analysis and Bioinformatics by : Martin Schäfer
Download or read book Finite Bayesian Mixture Models with Applications in Spatial Cluster Analysis and Bioinformatics written by Martin Schäfer and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models for Count Data by : Charalampos Chanialidis
Download or read book Bayesian Mixture Models for Count Data written by Charalampos Chanialidis and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pattern Recognition in Bioinformatics by : Marco Loog
Download or read book Pattern Recognition in Bioinformatics written by Marco Loog and published by Springer. This book was released on 2011-10-29 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Conference on Pattern Recognition in Bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers cover the wide range of possible applications of bioinformatics in pattern recognition: novel algorithms to handle traditional pattern recognition problems such as (bi)clustering, classification and feature selection; applications of (novel) pattern recognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. They are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.
Book Synopsis Bayesian Mixture Models for Density Estimation by : Huei-Wen Teng
Download or read book Bayesian Mixture Models for Density Estimation written by Huei-Wen Teng and published by . This book was released on 2009 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis New Development of Bayesian Mixture Models for Survival and Survey Data by : Yingmei Xi
Download or read book New Development of Bayesian Mixture Models for Survival and Survey Data written by Yingmei Xi and published by . This book was released on 2008 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear Mixture Models by : Tatiana V. Tatarinova
Download or read book Nonlinear Mixture Models written by Tatiana V. Tatarinova and published by . This book was released on 2014-12-31 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature. In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.
Book Synopsis Statistical Analysis of Microbiome Data by : Somnath Datta
Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Book Synopsis Multi-omics Profiling of Unique Niches to Reveal the Microbial and Metabolite Composition by : Roshan Kumar
Download or read book Multi-omics Profiling of Unique Niches to Reveal the Microbial and Metabolite Composition written by Roshan Kumar and published by Frontiers Media SA. This book was released on 2022-09-29 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Split-merge Techniques for Bayesian Mixture Models by : Sonia Jain
Download or read book Split-merge Techniques for Bayesian Mixture Models written by Sonia Jain and published by . This book was released on 2002 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models with Applications in Macroeconomics by : Chenghan Hou
Download or read book Bayesian Mixture Models with Applications in Macroeconomics written by Chenghan Hou and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A vast empirical literature has documented the widespread nature of structural instability in many macroeconomic time series. In order to accommodate such a feature, there has been an increasing interest in models that allow time-variation in the parameters. One important issue for modeling this time-variation is to decide which type of time-varying processes is more suitable in applications. For instance, one might want to choose between a model where the parameters are gradually evolving over time or one in which there are a small number of abrupt change-points. The objective of this thesis is to investigate the performance of Bayesian mixture models in modeling such changes in macroeconomic time series. First, we examine the performance of two basic types of mixture models, a scale mixture of Gaussian models and a finite Gaussian mixture model, in forecasting inflation rates of G7 countries. Since it is well-known that many heavy-tailed distributions can be represented as a scale mixture of Gaussian distributions, we build upon the frequently employed stochastic volatility (SV) models and allow the error terms to have different distributional assumptions, such as the $t$ distribution and double exponential (or Laplace) distribution. The results suggest that allowing for heavy-tailed distributed error terms is as important as allowing stochastic volatility in improving point and density forecast accuracy. Next, we propose a Gaussian mixture innovation model with time-varying mixture probabilities to detect the in-sample breaks in the relationship between inflation and inflation uncertainty. By allowing the time-variation in the mixture probabilities, we find that the proposed model produces more robust estimates and better in-sample fit. Our empirical study provides strong evidence of the existence of breaks in the relationship between inflation and inflation uncertainty in the last few decades. Finally, we develop a class of vector autoregressive (VAR) models with infinite hidden Markov structures. We first improve the computational efficiency by developing a new Markov chain Monte Carlo method built upon the precision-based algorithms. We then investigate the performance of these infinite hidden Markov models with various dynamics to predict the US inflation, GDP growth and interest rate. The results show that it is better to model separately the time variation in the conditional mean coefficients and that in the variance process.
Book Synopsis Insect Communities: Diversity Patterns and their Driving Forces by : Ai-Bing Zhang
Download or read book Insect Communities: Diversity Patterns and their Driving Forces written by Ai-Bing Zhang and published by Frontiers Media SA. This book was released on 2023-03-17 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models for Spectral Density Estimation by : Annalisa Cadonna
Download or read book Bayesian Mixture Models for Spectral Density Estimation written by Annalisa Cadonna and published by . This book was released on 2017 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a novel Bayesian modeling approach to spectral density estimation for multiple time series. Considering first the case of non-stationary time series, the log-periodogram of each series is modeled as a mixture of Gaussian distributions with frequency-dependent weights and mean functions. The implied model for the log-spectral density is a mixture of linear mean functions with frequency-dependent weights. The mixture weights are built through successive differences of a logit-normal distribution function with frequency-dependent parameters. Building from the construction for a single log-spectral density, we develop a hierarchical extension for multiple stationary time series. Specifically, we set the mean functions to be common to all log-spectral densities and model time series specific mixtures through the parameters of the logit-normal distribution. In addition to accommodating flexible spectral density shapes, a practically important feature of the proposed formulation is that it allows for ready posterior simulation through a Gibbs sampler with closed form full conditional distributions for all model parameters. We then extend the model to multiple locally stationary time series, a particular class of non-stationary time series, making it suitable for the analysis of time series with spectral characteristics that vary slowly with time. The modeling approach is illustrated with different types of simulated datasets, and used for spectral analysis of multichannel electroencephalographic recordings (EEGs), which provides a key motivating application for the proposed methodology.
Book Synopsis Bayesian Mixture Models for Frequent Itemset Mining by : Ruofei He
Download or read book Bayesian Mixture Models for Frequent Itemset Mining written by Ruofei He and published by . This book was released on 2012 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely used in the development of probability models in machine learning in recent years and these methods have many advantages, including their abilities to avoid overfitting. In this thesis, we develop two Bayesian mixture models with the Dirichlet distribution prior and the Dirichlet process (DP) prior to improve the previous non-Bayesian mixture model developed for transaction dataset mining. First, we develop a finite Bayesian mixture model by introducing conjugate priors to the model. Then, we extend this model to an infinite Bayesian mixture using a Dirichlet process prior. The Dirichlet process mixture model is a nonparametric Bayesian model which allows for the automatic determination of an appropriate number of mixture components. We implement the inference of both mixture models using two methods: a collapsed Gibbs sampling scheme and a variational approximation algorithm. Experiments in several benchmark problems have shown that both mixture models achieve better performance than a non-Bayesian mixture model. The variational algorithm is the faster of the two approaches while the Gibbs sampling method achieves a more accurate result. The Dirichlet process mixture model can automatically grow to a proper complexity for a better approximation. However, these approaches also show that mixture models underestimate the probabilities of frequent itemsets. Consequently, these models have a higher sensitivity but a lower specificity.