A Simple and General Exponential Family Framework for Partial Membership and Factor Analysis
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Edoardo M. Airoldi | Stephen E. Fienberg | Katherine A. Heller | Zoubin Ghahramani | David M. Blei | Elena A. Erosheva | Shakir Mohamed | Zoubin Ghahramani
[1] S. Newcomb. A Generalized Theory of the Combination of Observations so as to Obtain the Best Result , 1886 .
[2] M. Woodbury,et al. Clinical Pure Types as a Fuzzy Partition , 1974 .
[3] M. Woodbury,et al. Mathematical typology: a grade of membership technique for obtaining disease definition. , 1978, Computers and biomedical research, an international journal.
[4] James C. Bezdek,et al. Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.
[5] M. Woodbury,et al. A New Procedure for Analysis of Medical Classification , 1982, Methods of Information in Medicine.
[6] A. Kennedy,et al. Hybrid Monte Carlo , 1988 .
[7] B. Singer. Grade of Membership Representations: Concepts and Problems , 1989 .
[8] M. Woodbury,et al. Classification of depression by grade of membership: a confirmation study , 1989, Psychological Medicine.
[9] Donald L. Iglehart,et al. Probability, Statistics, and Mathematics: Papers in Honor of Samuel Karlin , 1989 .
[10] R Chuit,et al. The status of transmission of Trypanosoma cruzi in an endemic area of Argentina prior to control attempts, 1985. , 1991, Annals of tropical medicine and parasitology.
[11] B. Carlin,et al. Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .
[12] Shigeo Abe,et al. Neural Networks and Fuzzy Systems , 1996, Springer US.
[13] K. Manton,et al. The dynamics of dimensions of age-related disability 1982 to 1994 in the U.S. elderly population. , 1998, The journals of gerontology. Series A, Biological sciences and medical sciences.
[14] Michael E. Tipping. Probabilistic Visualisation of High-Dimensional Binary Data , 1998, NIPS.
[15] Christopher M. Bishop,et al. Bayesian PCA , 1998, NIPS.
[16] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[17] Tom Minka,et al. Automatic Choice of Dimensionality for PCA , 2000, NIPS.
[18] M. Knott,et al. Generalized latent trait models , 2000 .
[19] Sanjoy Dasgupta,et al. A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.
[20] Karl Bang Christensen. Latent Variable Models and Factor Analysis. Kendall's Library of Statistics 7, 2nd Edn. David Bartholomew and Martin Knott, Arnold, London, 1999. No. of pages: 224. Price: £35.00. ISBN 0‐340‐69243‐X , 2001 .
[21] Burton H. Singer,et al. Person-centered methods for understanding aging: The integration of numbers and narratives. , 2001 .
[22] M. Eisen,et al. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering , 2002, Genome Biology.
[23] Marcel Tanner,et al. POLYPARASITISM WITH SCHISTOSOMA MANSONI, GEOHELMINTHS, AND INTESTINAL PROTOZOA IN RURAL CÔTE D'IVOIRE , 2002, The Journal of parasitology.
[24] Carolyn Pillers Dobler,et al. Mathematical Statistics , 2002 .
[25] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[26] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[27] Michael A. West,et al. BAYESIAN MODEL ASSESSMENT IN FACTOR ANALYSIS , 2004 .
[28] J. Lafferty,et al. Mixed-membership models of scientific publications , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[29] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[30] Noah Kaplan,et al. Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation , 2005, Political Analysis.
[31] Burton H. Singer,et al. Malaria risk on the Amazon frontier , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[32] K. Manton,et al. Change in chronic disability from 1982 to 2004/2005 as measured by long-term changes in function and health in the U.S. elderly population , 2006, Proceedings of the National Academy of Sciences.
[33] M. Tanner,et al. An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[34] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[35] Thomas Hofmann,et al. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation , 2007 .
[36] S. Fienberg,et al. DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA. , 2007, The annals of applied statistics.
[37] K. Manton,et al. Recent declines in chronic disability in the elderly U.S. population: risk factors and future dynamics. , 2008, Annual review of public health.
[38] Ruslan Salakhutdinov,et al. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.
[39] Katherine A. Heller,et al. Bayesian Exponential Family PCA , 2008, NIPS.
[40] Katherine A. Heller,et al. Statistical models for partial membership , 2008, ICML '08.
[41] Edoardo M. Airoldi,et al. Mixed Membership Stochastic Blockmodels , 2007, NIPS.
[42] H. Rue,et al. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .
[43] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[44] Aleks Jakulin,et al. Analyzing the U.S. Senate in 2003: Similarities, Clusters, and Blocs , 2009, Political Analysis.
[45] Johannes Fürnkranz,et al. Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , 2010, ICML.
[46] J. M. Sanz-Serna,et al. Optimal tuning of the hybrid Monte Carlo algorithm , 2010, 1001.4460.
[47] Mohammad Emtiyaz Khan,et al. Variational bounds for mixed-data factor analysis , 2010, NIPS.
[48] Stephen E Fienberg,et al. Reconceptualizing the classification of PNAS articles , 2010, Proceedings of the National Academy of Sciences.
[49] Michael I. Jordan,et al. Mixed Membership Matrix Factorization , 2010, ICML.
[50] Zoubin Ghahramani,et al. Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling , 2010, The Annals of Applied Statistics.
[51] Radford M. Neal. Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .
[52] Andrew Gelman,et al. Handbook of Markov Chain Monte Carlo , 2011 .
[53] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[54] Kenneth Manton,et al. Black/White Differences in Health Status and Mortality Among the Elderly , 1989, Demography.
[55] D. Dunson,et al. Sparse Bayesian infinite factor models. , 2011, Biometrika.
[56] P. Priouret,et al. Bayesian Time Series Models: Adaptive Markov chain Monte Carlo: theory and methods , 2011 .
[57] M. Z. Balge,et al. Assessing health impacts in complex eco-epidemiological settings in the humid tropics : the centrality of scoping , 2011 .
[58] B. Singer,et al. Chagas disease - risk assessment by an environmental approach in northern Argentina , 2011 .
[59] M. Girolami,et al. Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[60] Mirko S. Winkler,et al. Health impact assessment of industrial development projects: a spatio-temporal visualization. , 2012, Geospatial health.
[61] Mirko S. Winkler,et al. Enhancing impact: visualization of an integrated impact assessment strategy. , 2012, Geospatial health.
[62] Katherine A. Heller,et al. Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning , 2011, ICML.
[63] Thomas Brendan Murphy,et al. Mixed Membership Models for Exploring User Roles in Online Fora , 2012, ICWSM.
[64] Mirko S. Winkler,et al. Assessing health impacts in complex eco-epidemiological settings in the humid tropics: Modular baseline health surveys , 2012 .
[65] Nando de Freitas,et al. Adaptive Hamiltonian and Riemann Manifold Monte Carlo , 2013, ICML.
[66] Abdul Suleman,et al. AN EMPIRICAL COMPARISON BETWEEN GRADE OF MEMBERSHIP AND PRINCIPAL COMPONENT ANALYSIS , 2013 .
[67] April Galyardt,et al. Interpreting Mixed Membership Models: Implications of Erosheva’s Representation Theorem , 2014 .
[68] Elena A. Erosheva,et al. 2 A Tale of Two ( Types of ) Memberships : Comparing Mixed and Partial Membership with a Continuous Data Example , 2014 .
[69] Edoardo M. Airoldi,et al. A Mixed Membership Approach to the Assessment of Political Ideology from Survey Responses , 2014 .
[70] Babak Shahbaba,et al. Split Hamiltonian Monte Carlo , 2011, Stat. Comput..
[71] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[72] Abolghasem A. Raie,et al. Probabilistic principal component analysis for texture modelling of adaptive active appearance models and its application for head pose estimation , 2015, IET Comput. Vis..
[73] Kjell A. Doksum,et al. Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition , 2015 .