Faculty

Carey Priebe

Professor

Research Interests

  • Computational Statistics
  • Kernel and Mixture Estimates
  • Statistical Pattern Recognition
  • Statistical Image Analysis
  • Statistical Inference for High-Dimensional and Graph Data
Journal Articles
  • L. Chen., J. T. Vogelstein., V. Lyzinski., Priebe, C. (2016).  A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Cennectomes.  Worm.  5(2).  1.
  • Athreya, D., V. Lyzinski., D. J. Marchette., Priebe, C., D. L. Sussman., M. Tang. (2016).  A limit theorem for scaled eigenvectors of random dot product graphs.  Sankhya A: A Indian Journal of Statistics.  Sankhya.  78-A(1).  1-18.
  • Tang, M., Athreya, D., Sussman, D., Lyzinski, V., Park, Y., Priebe, C. (2016).  A Semiparametric Two-Sample Hypothesis Testing for Random Dot Product Graphs.  Journal of Computational and Graphical Statistics.
  • Lyzinski, V., Sussman, D., Athreya, D., Park, Y., Priebe, C. (2016).  Community Detection and Classification in Hierarchical Stochastic Blockmodels.  IEEE Transactions on Network Science and Engineering.
  • Suwan, S., Lee, D. S., Tang, R., Sussman, D. L., Tang, M., Priebe, C. (2016).  Empirical Bayes Estimation for the Stochastic Blockmodel.  Electronic Journal of Statistics.  10(1).  761-782.
  • Lyzinski, V., Park, Y., Priebe, C., Trosset, M. W. (2016).  Fast Embedding for JOFC Using the Raw Stress Criterion.  Journal of Computational and Graphical Statistics.
  • Tang, R., Ketcha, M., Vogelstein, J., Priebe, C., Sussman, D. L. (2016).  Law of Large Graphs.
  • Tang, M., Priebe, C. (2016).  Limit Theorems for Eigenvectors of the Normalized Laplacian for Random Graphs.
  • Athreya, D., Tang, M., Lyzinski, V., Park, Y., Lewis, B., Kane, M., Priebe, C. (2016).  Numerical Tolerance for Spectral Decompositions of Random Dot Product Graphs.
  • Lyzinksi, V., Levin, K., Fishkind, D., Priebe, C. (2016).  On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Likelihood Estimation and Graph Matching.  Journal of Machine Learning Research.  17(179).  1-34.
  • Fishkind, D., Shen, C., Park, Y., Priebe, C. (2016).  On the Incommensurability Phenomenon.  Journal of Classification.  33(2).  185-209.
  • Trosset, M. W., Gao, M. (2016).  On the Power of Likelihood Ratio Tests in Dimension-Restricted Submodels.
  • Megarry, W., Cooney, C., D., C., Priebe, C. (2016).  Posterior Probability Modelling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands.  Remote Sensing.  8(529).  1-18.
  • Qin, Y., Priebe, C. (2016).  Robust Hypothesis Testing via Lq-Likelihood.  Statistica Sinica.
  • Zheng, D., Park, Y., Lyzinski, V., Vogelstein, J., Priebe, C., Burns, R. (2016).  Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs.  IEEE Transactions on Parallel and Distributed Systems.
  • Yoder, J., Priebe, C. (2016).  Semi-supervised K-means++.
  • Eichler, K., Li, F., Park, Y., Andrade, I., Schneider-Mizell, C., Huser, A., Saumweber, T., Huser, A., Bonnery, D., Gerber, B., Fetter, R. D., Truman, J. W., Priebe, C., Abbott, L. F., Thum, A., Zlatic, M., Cardona, A. (2016).  The Complete Wiring Diagram of a High-Order Learning and Memory Center, the Insect Mushroom Body.  Nature.
  • Adali, S., Priebe, C. (2016).  Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities.  Journal of Classification.  33(3).  485-506.
  • L. Chen., C. Shen., J. V. Vogelstein., Priebe, C. (2016).  Robust Vertex Classification.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(3).  579-590.
  • Lee, N., Tang, R., Priebe, C., Rosen, M. (2016).  A Model Selection Approach for Clustering a Multinomial Sequence with Non-Negative Factorization.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(12).  2345-2358.
  • Priebe, C. (2015).  A model selection approach for clustering a multinomial sequence with non-negative factorization.  IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • Yoder, J., Priebe, C. (2015).  A Model-Based Semi-Supervised Clustering Methodology.
  • Tang, M., Athreya, D., D. L. Sussman., V. Lyzinski., Priebe, C. (2015).  A Nonparametric Two-Sample Hypothesis Testing for Random Dot Product Graphs.  Bernoulli Journal.
  • W. G. Roncal., D. M. Kleissas., J. T. Vogelstein., P. Manavalan., R. Burns., R. J. Vogelstein., Priebe, C., M. A. Chevillet., Hager, G. (2015).  An Automated Images-To-Graphs Pipeline For High Resolution Connectomics.  Frontiers in Meuroinformatics.
  • Rosen, M. A., Dietz, A. S., Yang, T., Priebe, C., Pronovost, P. J. (2015).  An integrative framework for sensor-based measurement of teamwork in healthcare..  Journal of the American Medical Informatics Association .  22(1).  11-18.
  • J. T. Vogelstein., J. M. Conroy., V. Lyzinski., L. J. Podrazik., S. G. Kratzer., E. T. Harley., D. E. Fishkind., R. J. Vogelstein., Priebe, C. (2015).  Fast Approximate Quadradic Programming for Large (Brain) Graph Matching.  PLOS ONE.
  • S. Adali., Priebe, C. (2015).  Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities.  Journal of Classification.  33(3).  485-506.
  • V. Lyzinski., Fishkind, D., M. Fiori., Vogelstein, J., Priebe, C., G. Sapiro. (2015).  Graph Matching: Relax at Your Own Risk.  IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • Shen, C., Priebe, C. (2015).  Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection.
  • D. J. Marchette., S. Y. Choi., A. Rukhin., Priebe, C. (2015).  Neighborhood Homogeneous Labelings of Graphs.  Journal of Combinatorial Mathematics and Combinatorial Computing.  93.  201-220.
  • Fishkind, D., C. Shen., Y. Park., Priebe, C. (2015).  On the Incommensurability Phenomenon.  Journal of Classification.
  • J. T. Vogelstein., Priebe, C. (2015).  Shuffled Graph Classification: Theory and Connectome of Graphs.  Journal of Classification.  32.  3-20.
  • Shen, C., Chen, L., Priebe, C. (2015).  Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption.
  • Priebe, C., D. L. Sussman., M. Tang., J. T. Vogelstein. (2015).  Statistical inference on errorfully observed graphs.  Journal of Computational and Graphical Statistics.  24(4).  930-953.
  • D. E. Fishkind., V. Lyzinski., H. Pao., L. Chen., Priebe, C. (2015).  Vertex Nomination Schemes for Membership Prediction.  Annals of Applied Statistics.  9(3).  1510-1532.
  • V. Lyzinski., D. L. Sussman., D. E. Fishkind., H. Pao., L. Chen., J. T. Vogelstein., Y. Park., Priebe, C. (2015).  Spectral Clustering for Divide-and-Conquer Graph Matching.  Parallel Computing.  47.  70-87.
  • N. Kasthuri., K. J. Hayworth., D. R. Berger., R. L. Schalek., J. A. Conchello ., S. Knowles-Barley., D. Lee., A. Vásquez-Réina., V. Kaynig., T. R. Jones., M. Roberts., J. L. Morgan., J. C. Tapia., H. S. Seung., W. G. Roncal., J. T. Vogelstein., R. Burns., D. L. Sussman., Priebe, C., H. Pfister., J. W. Lichtman. (2015).  Saturated Reconstruction of a Volume of Neocortex.  Cell.  162 (3).  648-661.
  • S. Suwan., D. S. Lee., Priebe, C. (2015).  Bayesian Vertex Nomination Using Content and Context.  WIREs Computational Statistics .  7(6).  400-416.
  • M.A. Tang., A.S. Dietz., T. Yang., Priebe, C., P.J. Pronovost. (2015).  An Integrative Framework for Sensor-based Measurement of Teamwork in Healthcare.  Journal of the American Medical Informatics Association.  22(1).  11-18.
  • Alkaya, A. F., Aksakalli, V., Priebe, C. (2015).  A penalty search algorithm for the obstacle neutralization problem..  Computers & Operations Research.  53.  165–175.
  • Athreya, A., Lyzinski, V., Marchette, D. J., Priebe, C. E., Sussman, D. L., Tang, M. (2014).  A limit theorem for scaled eigenvectors of random dot product graphs.  Sankhya.
  • D.L.Sussman., M.Tang., Priebe, C. (2014).  Consistent latent position estimation and vertex clasifcation for random dot product graphs.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  36.  48–57.
  • C.Shen., M.Sun., M.Tang., Priebe, C. (2014).  Generalized canonical correlation analysis for classification in high dimensions.  Journal of Multivariate Analysis.  130.  310–322.
  • Shen, C., Sun, M., Tang, M., Priebe, C. (2014).  Generalized canonical correlation analysis for classification..  J. Multivariate Analysis ().  130.  310–322.
  • H.Wang., M.Tang., Park, Y., Priebe, C. (2014).  Locality statistics for anomaly detection in time-series of graphs.  IEEE Transactions on Signal Processing.  62.  703–717.
  • V.Lyzinski., D.L.Sussman., M.Tang., Athreya, D., Priebe, C. (2014).  Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding.  Electronic Journal of Statistics.  8.  2905–2922.
  • Lyzinski, V., Sussman, D., Tang, M., Athreya, A., Priebe, C. E. (2014).  Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding.  Electronic Journal of Statistics.  8(2).  2905-2922.
  • Lyzinski, V., Fishkind, D., Priebe, C. (2014).  Seeded graph matching for correlated Erdos-Renyi graphs.  Journal of Machine Learning Research.  15(Nov).  3513-3540.
  • Priebe, C., D.L.Sussman., M.Tang., Vogelstein, J. (2014).  Statistical inference on errorfully observed graphs.  Journal of Computational and Graphical Statistics.
  • Vogelstein, J., Park, Y., Ohyama, T., Kerr, R. A., Truman, J. W., Priebe, C., Zlatic, M. (2014).  Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning..  Science.  344(6182).  386–392.
  • Wang, H., Tang, M., Park, Y., Priebe, C. (2014).  Locality Statistics for Anomaly Detection in Time Series of Graphs.  Ieee Transactions on Signal Processing.  62(3).  703–717.
  • Sussman, D. L., Tang, M., Priebe, C. (2014).  Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs.  IEEE transactions on pattern analysis and machine intelligence.  36(1).  48–57.
  • M.Tang., Park, Y., Lee, N., Priebe, C. (2013).  Attribute fusion in a latent process model for time series of graphs.  IEEE Transactions in Signal Processing.  61.  1721–1732.
  • Fishkind, D., D.L.Sussman., M.Tang., Vogelstein, J., Priebe, C. (2013).  Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown.  Siam Journal on Matrix Analysis and Applications.  34.  23–39.
  • Sun, M., Priebe, C. (2013).  Efficiency investigation of manifold matching for text document classification.  Pattern Recognition Letters.  34(11).  1263–1269.
  • M.Sun., Priebe, C., M.Tang. (2013).  Generalized canonical correlation analysis for disparate data fusion.  Pattern Recognition Letters.  34.  194–200.
  • Marchette, D., Choi, S., Rukhin, A., Priebe, C. (2013).  Neighborhood Homogeneous Labelings of Graphs.  Journal of Combinatorial Mathematics and Combinatorial Computing.  accepted for publication.
  • Lee, N., J.Yoder., M.Tang., Priebe, C. (2013).  On latent position inference from doubly stochastic messaging activities.  Multiscale Modeling and Simulation.  11.  683–718.
  • Priebe, C., Vogelstein, J., Bock, D. (2013).  Optimizing the Quantity/Quality Trade-Off in Connectome Inference.  Communications in Statistics-Theory and Methods.  42(19).  3455–3462.
  • Vogelstein, J., Priebe, C. (2013).  Shuffled Graph Classification: Theory and Connectome Applications.  Journal of Classification.
  • M.Tang., D.L.Sussman., Priebe, C. (2013).  Universally consistent vertex classification for latent positions graphs.  Annals of Statistics..  31.  1406–1430.
  • Qin, Y., Priebe, C. (2013).  Maximum Lq-Likelihood Estimation via the Expectation-Maximization Algorithm: A Robust Estimation of Mixture Models.  Journal of the American Statistical Association.  108(503).  914–928.
  • Priebe, C., Marchette, D. J., Ma, Z., Adali, S. (2013).  Manifold matching: Joint optimization of fidelity and commensurability.  Brazilian Journal of Probability and Statistics.  27(3).  377–400.
  • Vogelstein, J., Gray, W. R., Vogelstein, R. J., Priebe, C. (2013).  Graph Classification using Signal Subgraphs: Applications in Statistical Connectomics.  IEEE transactions on pattern analysis and machine intelligence.  35(7).  1539-1551.
  • Tang, M., Sussman, D. L., Priebe, C. (2013).  Universally Consistent Vertex Classification for Latent Positions Graphs.  Annals of Statistics.  41(3).  1406–1430.
  • Athreya, A., Lyzinski, V., Marchette, D. J., Priebe, C., Sussman, D. L., Tang, M. (2013).  A central limit theorem for scaled eigenvectors of random dot product graphs.  arXiv.org.
  • Lee, N., Tang, M., Yoder, J., Priebe, C. (2013).  On latent position inference from doubly stochastic messaging activities.  Multiscale Modeling & Simulation.  11(3).  683-718.
  • Tang, M., Park, Y., Lee, N., Priebe, C. (2013).  Attribute fusion in a latent process model for timeseries of graphs.  IEEE Transactions on Signal Processing.  61(7).  1721-1732.
  • Lyzinski, V., Fishkind, D., Priebe, C. (2013).  Seeded graph matching for correlated Erdos-Rényi graphs.  arXiv.org.
  • Park, Y., Priebe, C., Youssef, A. (2013).  Anomaly Detection in Time Series of Graphs using Fusion of Graph Invariants.  IEEE Journal of Selected Topics in Signal Processing.  7(1).  67-75.
  • Sun, M., Priebe, C., Tang, M. (2013).  Generalized Canonical Correlation Analysis for Disparate Data Fusion.  Pattern Recognition Letters.  194–200.
  • Sussman, D. L., Tang, M., Fishkind, D., Priebe, C. (2012).  A consistent adjacency spectral embedding for stochastic blockmodel graphs.  Journal of the American Statistical Association.  1119–1128.
  • Fishkind, D., Sussman, D. L., Tang, M., Vogelstein, J., Priebe, C. (2012).  Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown.  SIAM Journal on Matrix Analysis and Applicationsins.  stat.ME.
  • Ma, Z., Marchette, D. J., Priebe, C. (2012).  Fusion and inference from multiple data sources in a commensurate space.  Statistical Analysis and Data Mining.  5(3).  187–193.
  • Rukhin, A., Priebe, C. (2012).  On the limiting distribution of a graph scan statistic.  Communications in Statistics - Theory and Methods.  41(7).  1151–1170.
  • Priebe, C., Solka, J. L., Marchette, D. J. (2012).  Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the potential for collaboration at the interface.  Statistical Analysis and Data Mining.  5.  178–186.
  • Lee, N., Priebe, C. (2011).  A latent process model for time series of attributed random graphs.  Statistical inference for stochastic processes.  14(3).  231–253.
  • Vogelstein, J., Vogelstein, R. J., Priebe, C. (2011).  Are mental properties supervenient on brain properties?.  Scientific Reports.  1.
  • Priebe, C. (2011).  Fisher’s conditionality principle in statistical pattern recognition.  The American Statistician.  65(3).  167–169.
  • Pao, H., Coppersmith, G. A., Priebe, C. (2011).  Statistical inference on random graphs: Comparative power analyses via Monte Carlo.  Journal of Computational and Graphical Statistics.
  • Yang, T., Priebe, C. (2011).  The Effect of Model Misspecification on Semi-Supervised Classification.  IEEE transactions on pattern analysis and machine intelligence.  33(10).  2093–2103.
  • Aksakalli, V., Fishkind, D., Priebe, C., Ye, X. (2011).  The Reset Disambiguation Policy for Navigating Stochastic Obstacle Fields.  Naval Research Logistics.  58(4).  389–399.
  • Rukhin, A., Priebe, C. (2011).  A comparative power analysis of the maximum degree and size invariants for random graph inference.  Journal of Statistical Planning and Inference.  141(2).  1041–1046.
  • Ye, X., Fishkind, D., Abrams, L., Priebe, C. (2011).  Sensor information monotonicity in disambiguation protocols.  Journal of the Operational Research Society.  62(1).  142–151.
  • Ye, X., Priebe, C. (2010).  A Graph-Search Based Navigation Algorithm for Traversing A Potentially Hazardous Area with Disambiguation..  IJORIS.  1(3).  14–27.
  • Gupchup, J., Terzis, A., Ma, Z., Priebe, C. (2010).  Classification-Based Event Detection in Ecological Monitoring Sensor Networks.  Electronic Journal of Structural Engineering, Special Issue: Wireless Sensor Networks and Practical Applications.  36-44.
  • Vogelstein, J., Harshbarger, S., McLoughlin, M., Beaty, J., Yantis, S., Connor, C., Thakor, N., Priebe, C., Etienne-Cummings, R. (2010).  Research Program in Applied Neuroscience.  Johns Hopkins APL technical digest.  28(3).  222–223.
  • Grothendieck, J., Priebe, C., Gorin, A L. (2010).  Statistical inference on attributed random graphs: Fusion of graph features and content.  Computational Statistics and Data Analysis.  54.  1777–1790.
  • Priebe, C., Park, Y., Marchette, D. J., Conroy, J. M. (2010).  Statistical inference on attributed random graphs: Fusion of graph features and content: An experiment on time series of Enron graphs.  Computational Statistics and Data Analysis.  54.  1766–1776.
  • Ma, Z., Cardinal-Stakenas, A., Park, Y., Trosset, M. W., Priebe, C. (2010).  Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices.  Journal of Classification.  27(3).  307–321.
  • Blatz, J., Fishkind, D., Priebe, C. (2010).  Efficient, optimal stochastic-action selection when limited by an action budget.  Mathematical Methods of Operations Research.  72(1).  63–74.
  • Carliles, S., Budavari, T., Heinis, S., Priebe, C., Szalay, S. (2010).  Random Forests for Photometric Redshifts.  The Astrophysical Journal.  712.  511-515.
  • Rukhin, A., Priebe, C., Healy, Dennis M Jr. (2009).  On the monotone likelihood ratio property for the convolution of independent binomial random variables.  Discrete Applied Mathematics.  157(11).  2562–2564.
  • Mohan, N. R., Priebe, C., Park, Y., John, M. (2009).  Statistical Analysis of Hippocampus Shape Using a Modified Mann-Whitney-Wilcoxon Test..  FGIT-BSBT.  57(Chapter 7).  45–52.
  • Miller, M., Priebe, C., Qiu, A., Fischl, B., Kolasny, A., Brown, T., Park, Y., Ratnanather, J., Busa, E., Jovicich, J., Yu, P., Dickerson, B. C., Buckner, R. L., BIRN, M. (2009).  Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping..  Human brain mapping.  30(7).  2132–2141.
  • Priebe, C., Wallis, W. (2008).  On the anomalous behaviour of a class of locality statistics.  Discrete Mathematics.
  • Karakos, D., Khudanpur, S., Marchette, D. J., Papamarcou, A., Priebe, C. (2008).  On the minimization of concave information functionals for unsupervised classification via decision trees.  Statistics & Probability Letters.  78(8).  975–984.
  • Marchette, D. J., Priebe, C. (2008).  Scan statistics for interstate alliance graphs.  Connections.  2.  43–64.
  • Trosset, M. W., Priebe, C., Park, Y., Miller, M. (2008).  Semisupervised learning from dissimilarity data..  Computational Statistics and Data Analysis.  52(10).  4643–4657.
  • Park, Y., Priebe, C., Miller, M., Mohan, N. R., Botteron, K. N. (2008).  Statistical analysis of twin populations using dissimilarity measurements in hippocampus shape space.  Journal of biomedicine & biotechnology.  2008(1).  –5.
  • Trosset, M., Priebe, C. (2008).  The out-of-sample problem for classical multidimensional scaling.  Computational Statistics and Data Analysis.
  • LEE, N. A., Priebe, C., Miller, M., Ratnanather, J. (2008).  Validation of alternating Kernel mixture method: Application to tissue segmentation of cortical and subcortical structures.  Journal of Biomedicine & Biotechnology.  Article No.: 346129.
  • Giles, K. E., Trosset, M. W., Marchette, D. J., Priebe, C. (2008).  Iterative denoising.  Computational Statistics.  23(4).  497–517.
  • Marchette, D. J., Priebe, C. (2008).  Predicting unobserved links in incompletely observed networks.  Computational Statistics and Data Analysis.  52(3).  1373–1386.
  • John, M., Priebe, C. (2007).  A data-adaptive methodology for finding an optimal weighted generalized Mann-Whitney-Wilcoxon statistic.  Computational Statistics and Data Analysis.  51(9).  4337–4353.
  • Fishkind, D., Priebe, C., Giles, K. E., Smith, L. N., Aksakalli, V. (2007).  Disambiguation protocols based on risk simulation.  Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans.  37(5).  814–823.
  • Ceyhan, E., Priebe, C., Marchette, D. J. (2007).  A new family of random graphs for testing spatial segregation.  Canadian Journal of Statistics-Revue Canadienne De Statistique.  35(1).  27–50.
  • DeVinney, J., Priebe, C. (2006).  A new family of proximity graphs: Class cover catch digraphs..  Discrete Applied Mathematics.  154(14).  1975–1982.
  • Ceyhan, E., Priebe, C. (2006).  On the distribution of the domination number of a new family of parametrized random digraphs..  MASA.  1(4).  231–255.
  • Ceyhan, E., Priebe, C., Wierman, J. (2006).  Relative density of the random r-factor proximity catch digraph for testing spatial patterns of segregation and association.  Computational Statistics and Data Analysis.  50(8).  1925–1964.
  • Priebe, C., Miller, M., Ratnanather, J. (2006).  Segmenting magnetic resonance images via hierarchical mixture modelling.  Computational Statistics and Data Analysis.  50(2).  551–567.
  • Priebe, C., Marchette, D. J., Park, Y., Muise, R. R. (2006).  Application of integrated sensing and processing decision trees for target detection and localization on digital mirror array imagery.  Applied optics.  45(13).  3022–3030.
  • Eveland, C. K., Socolinsky, D. A., Priebe, C., Marchette, D. J. (2005).  A hierarchical methodology for class detection problems with skewed priors.  Journal of Classification.  22(1).  17–48.
  • Priebe, C., Conroy, J. M., Marchette, D. J., Park, Y. (2005).  Scan statistics on enron graphs.  Computational and Mathematical Organization Theory.  3.  229–247.
  • Ceyhan, E., Priebe, C. (2005).  The use of domination number of a random proximity catch digraph for testing spatial patterns of segregation and association.  Statistics & Probability Letters.  73(1).  37–50.
  • Priebe, C., Fishkind, D., Abrams, L., Piatko, C. D. (2005).  Random disambiguation paths for traversing a mapped hazard field.  Naval Research Logistics.  52(3).  285–292.
  • Beg, M. F., Ceritoglu, C., Kolasny, A. E., Priebe, C., Ratnanather, J., Yashinski, R., Younes, L., Yu, P., Jovicich, J., Buckner, R. L., others. (2004).  Biomedical Informatics Research Network: Multi-Site Processing Pipeline for Shape Analysis of Brain Structures.  Human Brain Mapping, 10th Annual Meeting; Budapest, Hungary.
  • Johannsen, D. A., Wegman, E. J., Solka, J. L., Priebe, C. (2004).  Simultaneous selection of features and metric for optimal nearest neighbor classification.  Communications in Statistics-Theory and Methods.  33(9).  2137–2157.
  • Priebe, C., Marchette, D. J., Healy, D. M. (2004).  Integrated sensing and processing decision trees..  IEEE transactions on pattern analysis and machine intelligence.  26(6).  699–708.
  • Abrams, L., Fishkind, D., Priebe, C. (2004).  The generalized spherical homeomorphism theorem for digital images..  IEEE transactions on medical imaging.  23(5).  655–657.
  • Miller, M., Hosakere, M., Barker, A R., Priebe, C., Lee, N., Ratnanather, J., Wang, L., Gado, M., Morris, J. C., Csernansky, J. G. (2003).  Labeled Cortical Mantle Distance Maps of the Cingulate Quantify Differences between Dementia of the Alzheimer Type and Healthy Aging.  Proceedings of the National Academy of Sciences of the United States of America.  100(25).  15172–15177.
  • Priebe, C., Solka, J. L., Marchette, D. J., Clark, B. T. (2003).  Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays.  Computational Statistics and Data Analysis.  43(4).  621–632.
  • Priebe, C., Marchette, D. J., DeVinney, J. G., Socolinsky, D. A. (2003).  Classification using class cover catch digraphs.  Journal of Classification.  20(1).  3–23.
  • Pilla, R., Tao, P., Priebe, C. (2003).  Adaptive Methods for Spatial Scan Analysis via Semiparametric Mixture Models.  Journal of Computational and Graphical Statistics.  12(2).  332–353.
  • Olson, T., Pang, J. S., Priebe, C. (2003).  A likelihood-MPEC approach to target classification.  Mathematical Programming.  96(1).  1–31.
  • Marchette, D. J., Priebe, C. (2003).  Characterizing the scale dimension of a high-dimensional classification problem.  Pattern Recognition.  36(1).  45–60.
  • Abrams, L., Fishkind, D., Priebe, C. (2002).  A proof of the spherical homeomorphism conjecture for surfaces.  IEEE transactions on medical imaging.  21(12).  1564–1566.
  • Solka, J. L., Priebe, C., Clark, B. T. (2002).  A Visualization Framework for the Analysis of Hyperdimensional Data..  Int. J. Image Graphics ().  2(1).  145–161.
  • Xie, J. D., Priebe, C. (2002).  A weighted generalization of the Mann-Whitney-Wilcoxon statistic.  Journal of Statistical Planning and Inference.  102(2).  441–466.
  • James, F., Priebe, C., Marchette, D. J. (2002).  Consistent Estimation Of Mixture Complexity.
  • Priebe, C., Naiman, D., Cope, L. M. (2001).  Importance sampling for spatial scan analysis: computing scan statistic p-values for marked point processes.  Computational Statistics and Data Analysis.  35(4).  475–485.
  • Priebe, C. (2001).  Olfactory Classification via Interpoint Distance Analysis..  IEEE transactions on pattern analysis and machine intelligence.  23(4).  404–413.
  • Priebe, C., DeVinney, J. G., Marchette, D. J. (2001).  On the distribution of the domination number for random class cover catch digraphs.  Statistics & Probability Letters.  55(3).  239–246.
  • Naiman, D., Priebe, C. (2001).  Computing Scan Statistic p Values Using Importance Sampling, with Applications to Genetics and Medical Image Analysis.  Journal of Computational and Graphical Statistics.  10(2).  296–328.
  • Priebe, C., Marchette, D. J. (2000).  Alternating kernel and mixture density estimates.  Computational Statistics and Data Analysis.  35(1).  43–65.
  • Lee, D., Priebe, C. (2000).  Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator.  Computational Statistics.  15(2).  169–181.
  • Xie, J. D., Priebe, C. (2000).  Generalizing the Mann-Whitney-Wilcoxon statistic.  Journal of Nonparametric Statistics.  12(5).  661–682.
  • Priebe, C., Cowen, L. J. (1999).  A generalized Wilcoxon-Mann-Whitney statistic.  Communications in Statistics-Theory and Methods.  28(12).  2871–2878.
  • Friedman, H. S., Priebe, C. (1999).  Smoothing bandwidth selection for response latency estimation.  Journal of Neuroscience Methods.  87(1).  13–16.
  • Friedman, H. S., Priebe, C. (1998).  Estimating stimulus response latency.  Journal of Neuroscience Methods.  83(2).  185–194.
  • Priebe, C., Chen, D. (1998).  Spatial scan density estimates.  Proc. SPIE Vol. 3371.  3371.  295.
  • Solka, J. L., Wegman, E. J., Priebe, C., Poston, W. L., Rogers, G. W. (1998).  Mixture structure analysis using the Akaike Information Criterion and the bootstrap.  Statistics and Computing.  8(3).  177–188.
  • Priebe, C., Olson, T., Jr, Dennis. (1997).  A Spatial Scan Statistic for Stochastic Scan Partitions.  Journal of the American Statistical Association.  92(440).  1476–1484.
  • Priebe, C., Marchette, D. J., Rogers, G. W. (1997).  Semiparametric nonhomogeneity analysis.  Journal of Statistical Planning and Inference.  59(1).  45–60.
  • Cowen, L. J., Priebe, C. (1997).  Randomized nonlinear projections uncover high-dimensional structure.  Advances in Applied Mathematics.  19(3).  319–331.
  • Poston, W., Wegman, E., Priebe, C., Solka, J. (1997).  A Deterministic Method for Robust Estimation of Multivariate Location and Shape.  Journal of Computational and Graphical Statistics.  6(3).  300–313.
  • Marchette, D. J., Lorey, R. A., Priebe, C. (1997).  An analysis of local feature extraction in digital mammography.  Pattern Recognition.  30(9).  1547–1554.
  • Wallet, B. C., Solka, J. L., Priebe, C. (1997).  A method for detecting microcalcifications in digital mammograms.  Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology.  10(3 Suppl 1).  136–139.
  • Priebe, C., Marchette, D. J., Rogers, G. W. (1997).  Segmentation of random fields via borrowed strength density estimation.  IEEE transactions on pattern analysis and machine intelligence.  19(5).  494–499.
  • Priebe, C. (1996).  Nonhomogeneity Analysis Using Borrowed Strength.  Journal of the American Statistical Association.  91(436).  1497–1503.
  • Marchette, D. J., Priebe, C., Rogers, G. W., Solka, J. L. (1996).  Filtered kernel density estimation.  Computational Statistics.  11(2).  95–112.
  • Lorey, R. A., Solka, J. L., Rogers, G. W., Marchette, D. J., Priebe, C. (1995).  Mammographic Computer-Assisted Diagnosis using Computational Statistics Pattern Recognition..  Real-Time Imaging ().  1(2).  95–104.
  • Rogers, G. W., Solka, J. L., Priebe, C. (1995).  A Pdp Approach to Localized Fractal Dimension Computation with Segmentation Boundaries.  Simulation.  65(1).  26–36.
  • Priebe, C., Solka, J. L., Lorey, R. A., Rogers, G. W., Poston, W. L., KALLERGI, M., QIAN, W., CLARKE, L. P., Clark, R. (1994).  The Application of Fractal Analysis to Mammographic Tissue Classification.  Cancer Letters.  77(2-3).  183–189.
  • Priebe, C. (1994).  Adaptive Mixtures.  Journal of the American Statistical Association.  89(427).  796–806.
  • Poston, W. L., Rogers, G. W., Priebe, C., Solka, J. L. (1994).  A Qualitative-Analysis of the Resistive Grid Kernel Estimator.  Pattern Recognition Letters.  15(3).  219–225.
  • Rogers, G. W., SOLKA, J., MALYEVAC, D. S., Priebe, C. (1993).  A Self-Organizing Network for Computing a Posteriori Conditional Class Probability.  Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans.  23(6).  1672–1682.
  • Woods, K. S., Solka, J. L., Priebe, C., Doss, C. C., Bowyer, K. W., Clarke, L. P. (1993).  Comparative evaluation of pattern recognition techniques for detection of microcalcifications.  Proc. SPIE Vol. 1905.  1905.  841.
  • Priebe, C., Marchette, D. J. (1993).  Adaptive Mixture Density-Estimation.  Pattern Recognition.  26(5).  771–785.
  • Rogers, G. W., Solka, J. L., Priebe, C., Szu, H. H. (1992).  Optoelectronic computation of waveletlike-based features.  Optical Engineering 31(09).  31.  1886.
  • Solka, J. L., Priebe, C., Rogers, G. W. (1992).  An Initial Assessment of Discriminant Surface Complexity for Power Law Features.  Simulation.  58(5).  311–318.
  • Priebe, C., Marchette, D. J. (1991).  Adaptive Mixtures - Recursive Nonparametric Pattern-Recognition.  Pattern Recognition.  24(12).  1197–1209.
  • Marchette, D. J., Priebe, C. (1990).  The Adaptive Kernel Neural Network.  Mathematical and Computer Modelling.  14.  328–333.
  • "Limit Theorems for Eigenvectors of the Normalized Laplacian for Random Graphs", Theoretical Foundations for Statistical Network Analysis.  Cambridge, England.  October 6, 2016
  • "Semiparametric Spectral Modeling of the Drosophila Connectome", Theoretical Foundations for Statistical Network Analysis.  Cambridge, England.  August 22, 2016
  • "Repeated Motif Hierarchical Stochastic Blockmodels", Graph Limits and Statistics.  Cambridge, England.  July 15, 2016
  • Community Detection and Classification in Hierarchical Stochastic Blockmodels.  England.  May 06, 2016
  • Community Detection and Classification in Hierarchical Stochastic Blockmodels.  London, England.  April 29, 2016
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