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Bayesian Group Factor Analysis [article]

Seppo Virtanen, Arto Klami, Suleiman A. Khan, Samuel Kaski
2011 arXiv   pre-print
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does.  ...  We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.  ...  Krister Wennerberg (FIMM at University of Helsinki) for helping us in interpretation of discovered factors for the drug structure-response application. We also thank Dr. Juha Salmitaival, Prof.  ... 
arXiv:1110.3204v1 fatcat:vx2kcxwbrrckhjjjpr33e2booy

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis [article]

Sikun Yang, Heinz Koeppl
2018 arXiv   pre-print
In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process  ...  Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including systems biology and neuroimaging  ...  In this paper, we aim to develop a collapsed variational inference algorithm for the nonparametric Bayesian group factor analysis model.  ... 
arXiv:1809.03566v2 fatcat:45bv42unoffwrhjndw27dhgcbu

Bayesian group latent factor analysis with structured sparsity [article]

Shiwen Zhao and Chuan Gao and Sayan Mukherjee and Barbara E Engelhardt
2015 arXiv   pre-print
We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of  ...  canonical correlation analysis.  ...  Bayesian GFA model is called BASS (Bayesian group factor Analysis with Structured Sparsity).  ... 
arXiv:1411.2698v2 fatcat:vl7sbemrzrak3plw6w2qbe3hma

Bayesian Group Nonnegative Matrix Factorization for EEG Analysis [article]

Bonggun Shin, Alice Oh
2012 arXiv   pre-print
We propose a generative model of a group EEG analysis, based on appropriate kernel assumptions on EEG data. We derive the variational inference update rule using various approximation techniques.  ...  With this motivation, we devise a generative mode of group EEG analysis, based on Bayesian nonnegative matrix factorization.  ...  To deal with this limitation, group-NMF [10] (GNMF) was proposed by modifying the cost functions of the standard NMF. The advantage of group analysis of EEG is twofold.  ... 
arXiv:1212.4347v1 fatcat:fzdhexfzenepnio6wbh6hzt7ie

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis

Sikun Yang, Heinz Koeppl
2018 Zenodo  
In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process  ...  Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including systems biology and neuroimaging  ...  In this paper, we aim to develop a collapsed variational inference algorithm for the nonparametric Bayesian group factor analysis model.  ... 
doi:10.5281/zenodo.1966177 fatcat:aphfvpgui5gqdjdlu5kbvbfhoe

A Primer on Bayesian Analysis for Experimental Psychopathologists

Angelos-Miltiadis Krypotos, Tessa F. Blanken, Inna Arnaudova, Dora Matzke, Tom Beckers
2017 Journal of Experimental Psychopathology  
A Primer on Bayesian Analysis for Experimental Psychopathologists Krypotos, A.-M.; Blanken, T.F.; Arnaudova, I.; Matzke, D.; Beckers, T.  ...  and group as between-subject factor.  ...  Advantages of Bayesian analysis over NHST Bayesian data analysis has important advantages that make it much more informative than NHST.  ... 
doi:10.5127/jep.057316 pmid:28748068 pmcid:PMC5524172 fatcat:wjauge7sjrfpbo52zu2b5fie6y

The Analysis and Prevent in Traffic Accidents Based on Bayesian Network

Zhu Xiang Xu, Yi Jiang, Fan Lin, Long Dai
2011 Advanced Engineering Forum  
In this paper, we will establish Bayesian Networks traffic accident analysis model by K2 algorithm, which can make accident probability prediction and accident diagnosis.K2 algorithm is known to all with  ...  high efficiency and accuracy, but it requires to obtain order first, so to get the reasonable node order, first use clustering algorithm to divide the nodes into groups, in groups the similarity is high  ...  , we make use of accident records of the road traffic accidents information collection project in the ministry, and build the traffic accident analysis Bayesian network model for rational analysis[12].  ... 
doi:10.4028/www.scientific.net/aef.1.21 fatcat:p7ffvcdfkfceznanh5iubbza7i

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data

Udo Boehm, Maarten Marsman, Dora Matzke, Eric-Jan Wagenmakers
2018 Behavior Research Methods  
Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.  ...  To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure.  ...  The y-axis shows the hierarchical log-Bayes factors for 200 simulations. Hierarchical Bayes factors constitute the correct Bayesian analysis of the simulated data.  ... 
doi:10.3758/s13428-018-1054-3 pmid:29949071 pmcid:PMC6096647 fatcat:2qtnlcssgff2vfo2zimv7353ja

Identifying Risk Factors for Female Cardiovascular Disease Patients in Malaysia: A Bayesian Approach

Nurliyana Juhan, Yong Zulina Zubairi, Zarina Mohd Khalid, Ahmad Syadi And Mahmood Zuhdi
2018 MATEMATIKA  
Bayesian logistic regression model provided a better understanding onthe associated risk factors of CVD for female patients which may help tailor preventionor treatment plans more effectively.  ...  This study aimed to identify associated risk factors in CVD among femalepatients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian lo-gistic regression and obtain a feasible model  ...  Bayesian univariate analysis are performed for female patients and seven variables are considered significant namely age group, smoking status, family history of CVD, dyslipidaemia, MI history, renal disease  ... 
doi:10.11113/matematika.v34.n3.1135 fatcat:cpqy5k3h2zagniktwvc3rprbya

Comparing Bayesian Statistics and Frequentist Statistics in Serious Games Research

Wim Westera
2021 International Journal of Serious Games  
Accordingly, the paper calls for more emphasis on Bayesian Statistics in serious games research and beyond, as to reduce the present domination by the Frequentist Paradigm.  ...  article presents three empirical studies on the effectiveness of serious games for learning and motivation, while it compares the results arising from Frequentist (classical) Statistics with those from Bayesian  ...  Bayesian analysis Next, a Bayesian independent t-test was applied to study the difference between groups.  ... 
doi:10.17083/ijsg.v8i1.403 fatcat:qqhnyopplzb6xmdfc5rnqxdvla

Vulnerability Characteristic Analysis of Urban Group Buildings Based on the Learning and Reasoning of Bayesian Network

YI-MENG ZHANG, HAI SUN, YA-ZHI ZHENG
2018 DEStech Transactions on Engineering and Technology Research  
, Bayesian network of structural vulnerability characteristics analysis of buildings based on Python is constructed and then the fitting prediction is performed.  ...  First of all, through the comparative analysis of seismic hazard factors of the sample building such as building structure types, building floors and years of construction after structure stress research  ...  Summary Based on the characteristics analysis of group building vulnerability through Bayesian network, the variables of each node and their logical relationship are determined.  ... 
doi:10.12783/dtetr/pmsms2018/24873 fatcat:k7tmyhye6vdr7i4rsnelesmcsy

Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP

Daniel S. Quintana, Donald R. Williams
2018 BMC Psychiatry  
This article provides an applied introduction to Bayesian inference with Bayes factors using JASP.  ...  JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface.  ...  The funder had no influence in the design, analysis, or interpretation of data or in the writing of the manuscript.  ... 
doi:10.1186/s12888-018-1761-4 pmid:29879931 pmcid:PMC5991426 fatcat:wzexcd4inzaifldht2qgtywh4e

Social Capital and Migrant Maternal Depression. A Multilevel Bayesian Latent Variable Spatial Logistic Regression in South Western Sydney, Australia

John Eastwood
2018 International Journal of Integrated Care  
The association between social, demographic and ecological factors and aggregated outcome variables were investigated using exploratory factor analysis and Bayesian methods Results: Migrant mothers had  ...  The exploratory factor analysis identified six latent constructs: neighbourhood adversity, social cohesion, health behaviours, housing quality, social services, and social capital.  ...  Suggestions for future research: Analysis using multilevel spatial latent class analysis will add to our understanding of our population groups are influenced by population level social capital.  ... 
doi:10.5334/ijic.s1050 fatcat:redsqinch5dwdaki2qlezzaj6m

Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances

G. Su, O.F. Christensen, L. Janss, M.S. Lund
2014 Journal of Dairy Science  
Bayesian variable selection models often predict more accurate genomic breeding values than genomic BLUP (GBLUP), but GBLUP is generally preferred 30 markers is a good strategy when using markers of the  ...  The analysis using the Bayesian models were performed using the Bayz package (http:// www.bayz.biz).  ...  Moreover, group-marker weighting with a group size of 30 markers resulted in the highest reliability of GEBV for all 5 weighting factors.  ... 
doi:10.3168/jds.2014-8210 pmid:25129495 fatcat:4pbt2eqzuzerfnc2mnygdgtt5q

A method for augmenting supersaturated designs

Qiao-Zhen Zhang, Hong-Sheng Dai, Min-Qian Liu, Ya Wang
2018 Journal of Statistical Planning and Inference  
After analysis of the initial experiment with several methods, factors are classified into three groups: primary, secondary, and potential according to the times that they have been identified.  ...  The focus is on those secondary factors since they have been identified several times but not so many that experimenters are sure that they are active, the proposed Bayesian D s -optimal augmented design  ...  Assume we have classified factors into primary, potential, secondary groups and reordered them after the analysis of initial design.  ... 
doi:10.1016/j.jspi.2018.06.006 fatcat:gfpwb5zevrf4ffivtusera473e
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