On Model Selection for Bayesian Networks and Sparse Logistic Regression

[1]  H. Jeffreys An invariant form for the prior probability in estimation problems , 1946, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[2]  Tal Pupko,et al.  A structural EM algorithm for phylogenetic inference , 2001, J. Comput. Biol..

[3]  David Maxwell Chickering,et al.  A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.

[4]  Wasserman,et al.  Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.

[5]  Constantin F. Aliferis,et al.  Algorithms for Large Scale Markov Blanket Discovery , 2003, FLAIRS.

[6]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[7]  W. Fitch Toward Defining the Course of Evolution: Minimum Change for a Specific Tree Topology , 1971 .

[8]  Ronald L. Graham,et al.  On the History of the Minimum Spanning Tree Problem , 1985, Annals of the History of Computing.

[9]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[10]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[11]  J. Neyman MOLECULAR STUDIES OF EVOLUTION: A SOURCE OF NOVEL STATISTICAL PROBLEMS* , 1971 .

[12]  Jorma Rissanen,et al.  Fisher information and stochastic complexity , 1996, IEEE Trans. Inf. Theory.

[13]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[14]  Jeffrey S. Morris,et al.  Epigenetic-Genetic Interactions in the APC/WNT, RAS/RAF, and P53 Pathways in Colorectal Carcinoma , 2008, Clinical Cancer Research.

[15]  Jukka Corander,et al.  The role of local partial independence in learning of Bayesian networks , 2016, Int. J. Approx. Reason..

[16]  George A. F. Seber,et al.  Linear regression analysis , 1977 .

[17]  Tomi Silander,et al.  Learning locally minimax optimal Bayesian networks , 2010, Int. J. Approx. Reason..

[18]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[19]  Mihai Albu,et al.  Testing methods on an artificially created textual tradition , 2006 .

[20]  Mark A. Pitt,et al.  Advances in Minimum Description Length: Theory and Applications , 2005 .

[21]  L. Cavalli-Sforza,et al.  PHYLOGENETIC ANALYSIS: MODELS AND ESTIMATION PROCEDURES , 1967, Evolution; international journal of organic evolution.

[22]  Tuomas Heikkilä,et al.  Evaluating methods for computer-assisted stemmatology using artificial benchmark data sets , 2009, Lit. Linguistic Comput..

[23]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[24]  M. Stone An Asymptotic Equivalence of Choice of Model by Cross‐Validation and Akaike's Criterion , 1977 .

[25]  J. Cavanaugh,et al.  Generalizing the derivation of the schwarz information criterion , 1999 .

[26]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[27]  Bin Yu,et al.  Estimating sparse models from multivariate discrete data via transformed Lasso , 2009, 2009 Information Theory and Applications Workshop.

[28]  M. Xiong,et al.  Test for interaction between two unlinked loci. , 2006, American journal of human genetics.

[29]  Jorma Rissanen,et al.  Strong optimality of the normalized ML models as universal codes and information in data , 2001, IEEE Trans. Inf. Theory.

[30]  M. Spencer,et al.  Phylogenetics of artificial manuscripts. , 2004, Journal of theoretical biology.

[31]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[32]  Tuomas Heikkilä,et al.  Compression-based Stemmatology: A Study of the Legend of St. Henry of Finland , 2005 .

[33]  Jason H. Moore,et al.  Chapter 11: Genome-Wide Association Studies , 2012, PLoS Comput. Biol..

[34]  D. Posada,et al.  Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. , 2004, Systematic biology.

[35]  Holger Schwender,et al.  Identification of SNP interactions using logic regression. , 2008, Biostatistics.

[36]  Yuan Zou Structural EM methods in phylogenetics and stemmatology , 2010 .

[37]  Tandy J. Warnow,et al.  Analyzing the Order of Items in Manuscripts of The Canterbury Tales , 2003, Computers and the Humanities.

[38]  M. Collard,et al.  Investigating cultural evolution through biological phylogenetic analyses of Turkmen textiles , 2002 .

[39]  David Posada,et al.  MODELTEST: testing the model of DNA substitution , 1998, Bioinform..

[40]  J. Friedman Multivariate adaptive regression splines , 1990 .

[41]  S. Vrieze Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). , 2012, Psychological methods.

[42]  Craig Boutilier,et al.  Context-Specific Independence in Bayesian Networks , 1996, UAI.

[43]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[44]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[45]  Christopher J. Howe,et al.  Dante's Monarchia as a test case for the use of phylogenetic methods in stemmatic analysis , 2008, Lit. Linguistic Comput..

[46]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[47]  Teemu Roos,et al.  Oral fairy tale or literary fake? Investigating the origins of Little Red Riding Hood using phylogenetic network analysis , 2016, Digit. Scholarsh. Humanit..

[48]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[49]  Judea Pearl,et al.  Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach , 1982, AAAI.

[50]  Marie desJardins,et al.  Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence , 2005, ECML.

[51]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[52]  A. Barron,et al.  Asymptotic minimax regret for data compression, gambling and prediction , 1997, Proceedings of IEEE International Symposium on Information Theory.

[53]  Yuan Zou,et al.  Analysis of Textual Variation by Latent Tree Structures , 2011, 2011 IEEE 11th International Conference on Data Mining.

[54]  A. Barron,et al.  Jeffreys' prior is asymptotically least favorable under entropy risk , 1994 .

[55]  R. Tibshirani,et al.  A LASSO FOR HIERARCHICAL INTERACTIONS. , 2012, Annals of statistics.

[56]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[57]  N. Saitou,et al.  The neighbor-joining method: a new method for reconstructing phylogenetic trees. , 1987, Molecular biology and evolution.

[58]  Ingo Ruczinski,et al.  Logic Regression — Methods and Software , 2003 .

[59]  Andrey Rzhetsky,et al.  Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference , 1992, Journal of Molecular Evolution.

[60]  Mark J. van der Laan,et al.  Regulatory motif finding by logic regression , 2004, Bioinform..

[61]  Anne-Mieke Vandamme,et al.  The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis and Hypothesis Testing , 2009 .

[62]  Aki Vehtari,et al.  Comparison of Bayesian predictive methods for model selection , 2015, Stat. Comput..

[63]  Elizabeth A. Thompson,et al.  Human Evolutionary Trees , 1975 .

[64]  D. Posada jModelTest: phylogenetic model averaging. , 2008, Molecular biology and evolution.

[65]  Petri Myllymäki,et al.  A linear-time algorithm for computing the multinomial stochastic complexity , 2007, Inf. Process. Lett..

[66]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[67]  Tuomas Heikkilä,et al.  A Compression-Based Method for Stemmatic Analysis , 2006, ECAI.

[68]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[69]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[70]  Henry Tirri,et al.  On predictive distributions and Bayesian networks , 2000, Stat. Comput..