scholarly journals Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model

2021 ◽  
Vol 226 ◽  
pp. 111406
Author(s):  
Y.Q. Ni ◽  
R. Chen
Keyword(s):  
Mixture Model ◽  
Reliability Assessment ◽  
Strain Monitoring ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Bridge Reliability

Bayesian Mixture Model of Extended Redundancy Analysis

Psychometrika ◽  
2021 ◽  
Author(s):  
Minjung Kyung ◽  
Ju-Hyun Park ◽  
Ji Yeh Choi
Keyword(s):  
Mixture Model ◽  
Redundancy Analysis ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Extended Redundancy Analysis

A practical sampling approach for a Bayesian mixture model with unknown number of components

2007 ◽  
Vol 48 (4) ◽  
pp. 631-653 ◽  
Author(s):  
Liqun Wang ◽  
James C. Fu
Keyword(s):  
Mixture Model ◽  
Number Of Components ◽  
Unknown Number ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Sampling Approach

scholarly journals Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenyu Jia ◽  
Shizhong Xu
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Control Treatment ◽  
Microarray Gene Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Truncated Normal ◽  
Upregulated Genes

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.

Bayesian Mixture Model for Prediction of Bus Arrival Time

2015 ◽  
Vol 6 (6) ◽  
pp. 961
Author(s):  
Misbahuddin Misbahuddin ◽  
Riri Fitri Sari
Keyword(s):  
Mixture Model ◽  
Arrival Time ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture

scholarly journals A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Tingting Wang ◽  
Yi-Ping Phoebe Chen ◽  
Phil J. Bowman ◽  
Michael E. Goddard ◽  
Ben J. Hayes
Keyword(s):  
Qtl Mapping ◽  
Mixture Model ◽  
Genomic Prediction ◽  
Sampling Algorithm ◽  
Mcmc Sampling ◽  
Model Based ◽  
Bayesian Mixture Model ◽  
Expectation Maximisation ◽  
Bayesian Mixture

Bayesian mixture model estimation of aerosol particle size distributions

2011 ◽  
Vol 22 (1) ◽  
pp. 23-34 ◽  
Author(s):  
D. Wraith ◽  
C. Alston ◽  
K. Mengersen ◽  
T. Hussein
Keyword(s):  
Particle Size ◽  
Mixture Model ◽  
Aerosol Particle ◽  
Model Estimation ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Aerosol Particle Size

A Bayesian mixture model for clustering circular data

2020 ◽  
Vol 143 ◽  
pp. 106842
Author(s):  
Carlos E. Rodríguez ◽  
Gabriel Núñez-Antonio ◽  
Gabriel Escarela
Keyword(s):  
Mixture Model ◽  
Circular Data ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture
Sign in / Sign up

Export Citation Format

Share Document