Interpreting Mixed Membership

[1]  Ata Kabán,et al.  Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains , 2005, Data Mining and Knowledge Discovery.

[2]  S. Shringarpure Statistical Methods for studying Genetic Variation in Populations , 2012 .

[3]  Korbinian Moeller,et al.  Children's early mental number line: logarithmic or decomposed linear? , 2009, Journal of experimental child psychology.

[4]  P. Onghena,et al.  The relationship between the shape of the mental number line and familiarity with numbers in 5- to 9-year old children: evidence for a segmented linear model. , 2008, Journal of experimental child psychology.

[5]  Elena A. Erosheva,et al.  A Tale of Two (Types Of) Memberships , 2014, Handbook of Mixed Membership Models and Their Applications.

[6]  Robert S. Siegler,et al.  The Logarithmic-To-Linear Shift: One Learning Sequence, Many Tasks, Many Time Scales , 2009 .

[7]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[8]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  M. C. Jones,et al.  The Statistical Analysis of Compositional Data , 1986 .

[10]  Edoardo M. Airoldi,et al.  A Simple and General Exponential Family Framework for Partial Membership and Factor Analysis , 2014 .

[11]  Daniel Manrique-Vallier,et al.  Longitudinal mixed membership models with applications to disability survey data , 2010 .

[12]  L. Wasserman,et al.  CATS , 2005 .

[13]  April Galyardt,et al.  Mixed Membership Distributions with Applications to Modeling Multiple Strategy Usage. , 2012 .

[14]  P. Donnelly,et al.  Inference of population structure using multilocus genotype data. , 2000, Genetics.

[15]  Arindam Banerjee,et al.  Mixed-membership naive Bayes models , 2011, Data Mining and Knowledge Discovery.

[16]  J. Lafferty,et al.  Mixed-membership models of scientific publications , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[17]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[18]  Julie L. Booth,et al.  Development of numerical estimation in young children. , 2004, Child development.

[19]  S. Fienberg,et al.  DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA. , 2007, The annals of applied statistics.

[20]  M. Woodbury,et al.  Mathematical typology: a grade of membership technique for obtaining disease definition. , 1978, Computers and biomedical research, an international journal.

[21]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[22]  Michael I. Jordan,et al.  Modeling annotated data , 2003, SIGIR.

[23]  J. O. Ramsay,et al.  Functional Data Analysis (Springer Series in Statistics) , 1997 .

[24]  R. Siegler,et al.  The Development of Numerical Estimation , 2003, Psychological science.

[25]  John D. Lafferty,et al.  A correlated topic model of Science , 2007, 0708.3601.

[26]  Colin Campbell,et al.  The Latent Process Decomposition of cDNA Microarray Data Sets , 2005, TCBB.

[27]  Robert S. Siegler,et al.  Representational change and children’s numerical estimation , 2007, Cognitive Psychology.

[28]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[29]  Kenneth G. Manton,et al.  Statistical applications using fuzzy sets , 1994 .