Improving in situ data acquisition using training images and a Bayesian mixture model

2016 ◽  
Vol 91 ◽  
pp. 49-63 ◽  
Author(s):  
Mohammad Javad Abdollahifard ◽  
Gregoire Mariethoz ◽  
Mohammadreza Pourfard
Keyword(s):  
Data Acquisition ◽  
Mixture Model ◽  
Training Images ◽  
In Situ Data ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture

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 Ant Colony Optimization for Data Acquisition Mission Planning

2014 ◽  
Vol 5 (2) ◽  
pp. 3-11 ◽  
Author(s):  
Giancarlo Colmenares ◽  
Fadi Halal ◽  
Marek B. Zaremba
Keyword(s):  
Data Acquisition ◽  
Ant Colony Optimization ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Current Data ◽  
Ant Colony ◽  
Continuous Space ◽  
In Situ Data ◽  
Water Pollutant

Abstract The probabilistic Ant Colony Optimization (ACO) approach is presented to solve the problem of designing an optimal trajectory for a mobile data acquisition platform. An ACO algorithm optimizes an objective function defined in terms of the value of the acquired data samples subject to different sets of constraints depending on the current data acquisition strategy. The analysis presented in this paper focuses on an environment monitoring system, which acquires in-situ data for precise calibration of a water quality monitoring system. The value of the sample is determined based on the concentration of the water pollutant, which in turn is obtained through processing of multi-spectral satellite imagery. Since our problem is defined in a continuous space of coordinates, and in some strategies each point is able to connect to any other point in the space, we adopted a hybrid model that involves a connection graph and also a spatial grid.

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