scholarly journals Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering

2020 ◽  
Vol 22 (3) ◽  
pp. 1107-1114
Author(s):  
Tina Košuta ◽  
Marta Cullell-Dalmau ◽  
Francesca Cella Zanacchi ◽  
Carlo Manzo
Keyword(s):  
Bayesian Analysis ◽  
Relative Abundance ◽  
Bayesian Approach ◽  
Super Resolution ◽  
Molecular Aggregates ◽  
Protein Clustering ◽  
Precise Quantification

A Bayesian approach enables the precise quantification of the relative abundance of molecular aggregates of different stoichiometry from segmented super-resolution images.

A Survey of Bayesian Data Mining

Data Mining ◽  
2011 ◽  
pp. 1-26 ◽  
Author(s):  
Stefan Arnborg
Keyword(s):  
Data Mining ◽  
Bayesian Analysis ◽  
Graphical Models ◽  
Bayesian Approach ◽  
Data Sets ◽  
Media Distribution ◽  
Dirichlet Prior ◽  
Approach Problem ◽  
The Bayesian Approach ◽  
Selection Of

This chapter reviews the fundamentals of inference, and gives a motivation for Bayesian analysis. The method is illustrated with dependency tests in data sets with categorical data variables, and the Dirichlet prior distributions. Principles and problems for deriving causality conclusions are reviewed, and illustrated with Simpson’s paradox. The selection of decomposable and directed graphical models illustrates the Bayesian approach. Bayesian and EM classification is shortly described. The material is illustrated on two cases, one in personalization of media distribution, one in schizophrenia research. These cases are illustrations of how to approach problem types that exist in many other application areas.

Super-resolution in cardiac MRI using a Bayesian approach

2013 ◽  
Author(s):  
Nelson Velasco Toledo ◽  
Andrea Rueda ◽  
Cristina Santa Marta ◽  
Eduardo Romero
Keyword(s):  
Bayesian Approach ◽  
Cardiac Mri ◽  
Super Resolution

A Bayesian Approach to the Stereo Correspondence Problem

2002 ◽  
Vol 14 (6) ◽  
pp. 1371-1392 ◽  
Author(s):  
Jenny C. A. Read
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Probabilistic Approach ◽  
Correspondence Problem ◽  
Stereo Correspondence ◽  
Retinal Position ◽  
Single Solution ◽  
Ill Posed ◽  
The Right ◽  
Limiting Case

I present a probabilistic approach to the stereo correspondence problem. Rather than trying to find a single solution in which each point in the left retina is assigned a partner in the right retina, all possible matches are considered simultaneously and assigned a probability of being correct. This approach is particularly suitable for stimuli where it is inappropriate to seek a unique partner for each retinal position—for instance, where objects occlude each other, as in Panum's limiting case. The probability assigned to each match is based on a Bayesian analysis previously developed to explain psychophysical data (Read, 2002). This provides a convenient way to incorporate constraints that enable the ill-posed correspondence problem to be solved. The resulting model behaves plausibly for a variety of different stimuli.

Bayesian Analysis of the Brown–Proschan Model

2015 ◽  
Vol 30 (1) ◽  
Author(s):  
Dinh Tuan Nguyen ◽  
Yann Dijoux ◽  
Mitra Fouladirad
Keyword(s):  
Weibull Distribution ◽  
Bayesian Analysis ◽  
Failure Rate ◽  
Bayesian Approach ◽  
Real Data ◽  
Posterior Distributions ◽  
Imperfect Maintenance ◽  
Maintenance Model ◽  
Informative Prior Distributions

AbstractThe paper presents a Bayesian approach of the Brown–Proschan imperfect maintenance model. The initial failure rate is assumed to follow a Weibull distribution. A discussion of the choice of informative and non-informative prior distributions is provided. The implementation of the posterior distributions requires the Metropolis-within-Gibbs algorithm. A study on the quality of the estimators of the model obtained from Bayesian and frequentist inference is proposed. An application to real data is finally developed.

Lifting A Veil On Diversity: A Bayesian Approach To Fitting Relative-Abundance Models

2006 ◽  
Vol 16 (1) ◽  
pp. 202-212 ◽  
Author(s):  
Duncan J. Golicher ◽  
Robert B. O'Hara ◽  
Lorena Ruíz-Montoya ◽  
Luis Cayuela
Keyword(s):  
Relative Abundance ◽  
Bayesian Approach

scholarly journals Introdução à estatística bayesiana aplicada à linguística

2021 ◽  
Author(s):  
Guilherme D. Garcia ◽  
Ronaldo Mangueira Lima Jr
Keyword(s):  
Data Analysis ◽  
Simple Model ◽  
Bayesian Analysis ◽  
Regression Model ◽  
Bayesian Approach ◽  
Linguistic Data ◽  
Bayesian Data Analysis ◽  
Frequentist Statistics

Neste artigo, apresentamos os conceitos básicos de uma análise estatística bayesiana e demonstramos como rodar um modelo de regressão utilizando a linguagem R. Ao longo do artigo, comparamos estatística bayesiana e estatística frequentista, destacamos as diferentes vantagens apresentadas por uma abordagem bayesiana, e demonstramos como rodar um modelo simples e visualizar efeitos de interesse. Por fim, sugerimos leituras adicionais aos interessados neste tipo de análise.In this paper, we introduce the basics of Bayesian data analysis and demonstrate how to run a regression model in R using linguistic data. Throughout the paper, we compare Bayesian and Frequentist statistics, highlighting the different advantages of a Bayesian approach. We also show how to run a simple model and how to visualize effects of interest. Finally, we suggest additional readings to those interested in Bayesian analysis more generally.

scholarly journals Bayesian analysis for genotype x environment interactions and the GGE-biplot assessment: Evaluation of balanced classifications with missing values

2015 ◽  
Vol 3 (2) ◽  
pp. 210-217
Author(s):  
Siraj Osman Omer ◽  
Eltayeb Hassan Slafab ◽  
Abhishek Rathore
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Missing Values ◽  
Pedigree Information ◽  
Gge Biplot ◽  
Growing Seasons ◽  
Frequentist Approach ◽  
Sorghum Variety ◽  
Wide Range ◽  
Estimation Of Variance

In multiple environmental trials (METs) most of the data, balanced or unbalanced, are normally tested over a wide range of environments (locations, years, growing seasons, etc.) and the basic statistical method used to obtain reliable statistical information. A case study is presented here to demonstrate the usefulness of Bayesian approach in genotype-by environment data analysis, in comparison with frequentist approach and GGE biplot assessment classification with missing value. Particular emphasis was given to Bayesian application that exploits pedigree information and to the analysis of GEI data for estimation of heritability, genetic gain and means prediction. A Markov Chain Monte Carlo (MCMC) method has been considered to perform Bayesian inference using R2WinBUGS. The study recently done in sorghum variety trials show investigation can be applied for multi environmental trial data. Results shows that the Bayesian estimation of variance components was accurate compared to the frequentist. The two principal components in GGEbiplot analysis were significant, explaining 95.13% (85.17% PC1 and 9.9.% PC2) for frequentist approach and explaining 97.36% (84.06% PC1 and 13.3% PC2) for Bayesian approach of interaction variation. Bayesian analysis indicates GGE-biplot gave the best results in contributing to the GEI. Bayesian approach for analysis GEI data is highly suitable with missing values.Int J Appl Sci Biotechnol, Vol 3(2): 210-217 DOI: http://dx.doi.org/10.3126/ijasbt.v3i2.11908  

scholarly journals Being Bayesian: Discussions from the Perspectives of Stakeholders and Hydrologists

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 461
Author(s):  
Ty P.A. Ferre
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Bayes Theorem ◽  
Standard Practice ◽  
Natural Approach ◽  
The Third ◽  
Optimal Observation ◽  
Bayes Theory ◽  
Hydrologic Analysis ◽  
Natural Choice

Bayes’ Theorem is gaining acceptance in hydrology, but it is still far from standard practice to cast hydrologic analyses in a Bayesian context—especially in the realm of hydrologic practice. Three short discussions are presented to encourage more complete adoption of a Bayesian approach. The first, aimed at a stakeholder audience, seeks to explain that an informal Bayesian analysis is the default approach that we all take to any decision made under uncertainty. The second, aimed at a general hydrologist audience, seeks to establish multi-model approaches as the natural choice for Bayesian hydrologic analysis. The goal of this discussion is to provide a bridge from the stakeholder’s natural approach to a more formal, quantitative Bayesian analysis. The third discussion is targeted to a more advanced hydrologist audience, suggesting that some elements of hydrologic practice do not yet reflect a Bayesian philosophy. In particular, an example is given that puts Bayes Theory to work to identify optimal observation sets before data are collected.

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