Friedman, Nir - School of Computer Science and Engineering, Hebrew University of Jerusalem

• Discriminative Learning of Composite Transcriptional Regulatory Modules
• Mean Field Variational Approximation for Continuous-Time Bayesian Ido Cohn Tal El-Hay Nir Friedman
• A Structural EM Algorithm for Phylogenetic Inference # Nir Friedman +
• Discovering Hidden Variables: A Structure-Based Approach
• Sequential Update of Bayesian Network Structure Nir Friedman
• Bayesian Q-learning Richard Dearden
• Model based Bayesian Exploration Richard Dearden
• Computational Aspects in Gene Expression Analysis
• BIOINFORMATICS Vol. 00 no. 00 2007
• Conditional Logics of Belief Change \Lambda Nir Friedman
• ContextSpecific Bayesian Clustering for Gene Expression Yoseph Barash
• On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks
• Gibbs Sampling in Factorized Continuous-Time Markov Processes Tal El-Hay Nir Friedman
• Exploring Transcription Regulation through Cell-to-cell Variability
• Overabundance Analysis and Class Discovery in Gene Expression Data \Lambda
• Tissue Classification with Gene Expression Profiles Amir Ben-Dor
• Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables
• Belief Revision: A Critique \Lambda Nir Friedman
• Discovering Hidden Variables: A StructureBased Approach
• Model based Bayesian Exploration Richard Dearden
• Learning the Dimensionality of Hidden Variables Gal Elidan Nir Friedman
• Translational Review Am. J. Respir. Cell Mol. Biol. Vol. 27, pp. 125132, 2002
• Belief Revision: A Critique Nir Friedman
• Learning Probabilistic Models of Relational Structure Lise Getoor GETOOR@CS.STANFORD.EDU
• Structured Representation of Complex Stochastic Systems Nir Friedman \Lambda
• Learning Module Networks Computer Science Dept.
• Data Perturbation for Escaping Local Maxima in Learning Gal Elidan and Matan Ninio and Nir Friedman
• A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor
• Likelihood Computations Using Value Abstraction Nir Friedman
• Sequential Update of Bayesian Network Structure Nir Friedman
• Agglomerative Multivariate Information Noam Slonim Nir Friedman Naftali Tishby
• A Structural EM Algorithm for Phylogenetic Inference Nir Friedman Matan Ninio
• "Ideal Parent" Structure Learning for Continuous Variable Networks Iftach Nachman1
• Using Bayesian Networks to Analyze Expression Data Nir Friedman
• Learning the Structure of Dynamic Probabilistic Networks Nir Friedman Kevin Murphy Stuart Russell
• Unsupervised Document Classification using Sequential Information Maximization
• Gibbs Sampling in Factorized Continuous-Time Markov Processes
• BIOINFORMATICS Vol. 1 no. 1 2001
• Data Analysis with Bayesian Networks: A Bootstrap Approach Nir Friedman
• Multivariate Information Bottleneck Nir Friedman Ori Mosenzon Noam Slonim Naftali Tishby
• Learning the Dimensionality of Hidden Variables Gal Elidan Nir Friedman
• Being Bayesian about Network Structure Nir Friedman
• Multivariate Information Bottleneck Nir Friedman Ori Mosenzon Noam Slonim Naftali Tishby
• Nondeterministic Actions and the Frame Problem Craig Boutilier
• Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting
• Data Analysis with Bayesian Networks: A Bootstrap Approach Nir Friedman
• Discretizing Continuous Attributes While Learning Bayesian Networks Nir Friedman
• Being Bayesian About Network Structure A Bayesian Approach to Structure Discovery in Bayesian Networks
• BIOINFORMATICS Vol. 1 no. 1 2001 Rich Probabilistic Models for Gene Expression
• Plausibility Measures and Default Reasoning Nir Friedman
• Vol. 00 no. 00 2006, pages 16 doi:10.1093/bioinformatics/btl304BIOINFORMATICS
• Context-Specific Bayesian Clustering for Gene Expression Yoseph Barash
• Learning Probabilistic Models of Link Structure Lise Getoor GETOOR@CS.UMD.EDU
• Modeling Dependencies in ProteinDNA Binding Sites Yoseph Barash 1 , Gal Elidan 1 , Nir Friedman 1# , Tommy Kaplan 1,2
• On the Sample Complexity of Learning Bayesian Networks Nir Friedman
• Class Discovery in Gene Expression Data Amir BenDor
• BIOINFORMATICS Vol. 1 no. 1 2001 Inferring Subnetworks from Perturbed
• LEARNING HIDDEN VARIABLES IN PROBABILISTIC GRAPHICAL MODELS
• Using Bayesian Networks to Analyze Expression Data Nir Friedman
• Learning Probabilistic Relational Models Nir Friedman
• A KnowledgeBased Framework for Belief Change, Part II: Revision and Update \Lambda
• Learning Symmetric Relational Markov Random Fields
• The Bayesian Structural EM Algorithm Nir Friedman \Lambda
• Learning Belief Networks in the Presence of Missing Values and Hidden Variables Nir Friedman
• Incorporating Expressive Graphical Models in Variational Approximations: ChainGraphs and Hidden Variables
• Discretizing Continuous Attributes While Learning Bayesian Networks Nir Friedman
• Generalized Prioritized Sweeping David Andre Nir Friedman Ronald Parr
• BIOINFORMATICS Vol. 00 no. 00 2004
• Gaussian Process Networks Nir Friedman
• Conditional Logics of Belief Change Nir Friedman
• From Promoter Sequence to Expression: A Probabilistic Framework
• Generalized Prioritized Sweeping David Andre Nir Friedman Ronald Parr
• On DecisionTheoretic Foundations for Defaults \Lambda Ronen I. Brafman and Nir Friedman
• A Qualitative Markov Assumption and Its Implications for Belief Change Nir Friedman
• Learning Probabilistic Models of Relational Structure Lise Getoor GETOOR@CS.STANFORD.EDU
• ``Ideal Parent'' Structure Learning for Continuous Variable Networks Iftach Nachman 1 Gal Elidan 1 Nir Friedman
• Where is the Impact of Bayesian Networks in Learning? Nir Friedman \Lambda
• Being Bayesian about Network Structure Nir Friedman
• FirstOrder Conditional Logic Revisited Nir Friedman
• Building Classifiers using Bayesian Networks Nir Friedman
• FirstOrder Conditional Logic Revisited \Lambda Nir Friedman
• Discovering the Hidden Structure of Complex Dynamic Systems Xavier Boyen
• Building Classifiers using Bayesian Networks Nir Friedman
• Gaussian Process Networks Nir Friedman
• Being Bayesian About Network Structure A Bayesian Approach to Structure Discovery in Bayesian Networks
• Tissue Classification with Gene Expression Profiles y Amir BenDor z
• Bayesian Qlearning Richard Dearden
• LEARNING BAYESIANNETWORKS WITH LOCAL STRUCTURE NIR FRIEDMAN
• Plausibility Measures and Default Reasoning Nir Friedman
• 1998, Nir Friedman, U.C. Berkeley, and Moises Goldszmidt, SRI International. All rights reserved. Learning Bayesian Networks from Data
• , , 1--37 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
• Histone Modifications in Transcriptional A thesis submitted in partial fulfillment of the
• Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Nir Friedman
• Continuous Time Markov Networks Tal El-Hay Nir Friedman
• Image segmentation in video sequences: A probabilistic approach
• On the Complexity of Conditional Logics \Lambda Nir Friedman
• Learning Bayesian Networks with Local Structure Nir Friedman
• Plausibility Measures: A User's Guide Nir Friedman
• Learning Bayesian Networks from Data
• Analysis of DNA Motifs Based on a Novel
• Tissue Classification with Gene Expression Profiles Amir BenDor \Lambda
• JOURNAL OF COMPUTATIONAL BIOLOGY Volume 13, Number 2, 2006
• Journal of Machine Learning Research 6 (2005) 81127 Submitted 9/04; Revised 12/04; Published 1/05 Learning Hidden Variable Networks
• The Bayesian Structural EM Algorithm Nir Friedman
• Belief Revision with Unreliable Observations Craig Boutilier \Lambda
• Using Bayesian Networks to Analyze Expression Data \Lambda Nir Friedman
• ContextSpecific Independence in Bayesian Networks Craig Boutilier
• Using Bayesian Networks to Analyze Expression Data Nir Friedman
• Data Perturbation for Escaping Local Maxima in Learning Gal Elidan and Matan Ninio and Nir Friedman
• Learning Bayesian Network Structure from Massive Datasets: The ``Sparse Candidate'' Algorithm
• Learning Belief Networks in the Presence of Missing Values and Hidden Variables Nir Friedman
• BIOINFORMATICS Vol. 1 no. 1 2001
• PROBABILISTIC MODELING OF GENE REGULATORY NETWORKS
• Efficient Learning using Constrained Sufficient Statistics Nir Friedman
• ContextSpecific Bayesian Clustering for Gene Expression Yoseph Barash
• Modeling Belief in Dynamic Systems. Part I: Foundations
• Plausibility Measures and Default Reasoning Nir Friedman
• Learning the Structure of Dynamic Probabilistic Networks Nir Friedman Kevin Murphy Stuart Russell
• Journal of Machine Learning Research 8 (2007) 1799-1833 Submitted 09/06; Revised 5/07; Published 8/07 "Ideal Parent" Structure Learning for
• Reasoning about Structured Stochastic Systems in Continuous-Time
• MODELING BELIEFS IN DYNAMIC SYSTEMS A DISSERTATION
• A KnowledgeBased Framework for Belief Change Part I: Foundations \Lambda
• Learning Module Networks Computer Science Dept.
• Transcription Regulation Models and Their Application to Human Disease Research
• Modeling Dependencies in Protein-DNA Binding Sites Yoseph Barash1
• FROM GENE EXPRESSION TO MOLECULAR PATHWAYS
• Learning Bayesian Networks from Data --AAAI 1998 Tutorial--
• Computational Methods in Systems Biology
• A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among
• Journal of Machine Learning Research 11 (2010) 2745-2783 Submitted 5/10; Revised 9/10; Published 10/10 Mean Field Variational Approximation
• Structure and function of a transcriptional network activated by the MAPK Hog1
• A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval
• Cell Cycle and Chaperone-Mediated Regulation of H3K56ac Incorporation in Yeast
• Journal of Machine Learning Research 6 (2005) 557588 Submitted 3/04; Revised 1/05; Published 4/05 Learning Module Networks
• ORIGINAL RESEARCH ARTICLE Peripheral blood mononuclear cell gene expression
• Learning Probabilistic Models of Link Structure Lise Getoor GETOOR@CS.UMD.EDU
• S72 AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY VOL. 31 2004 Identifying Regulatory Networks
• Convexifying the Bethe Free Energy Ofer Meshi Ariel Jaimovich Amir Globerson Nir Friedman
• Template Based Inference in Symmetric Relational Markov Random Fields Ariel Jaimovich Ofer Meshi Nir Friedman
• GeneXPress: A Visualization and Statistical Analysis Tool for Gene Expression and Sequence
• The Information Bottleneck EM Algorithm Gal Elidan and Nir Friedman
• R(t1) R(t2) L(t1) L(t2)
• Unsupervised Document Classification using Sequential Information Maximization
• Context-Specific Bayesian Clustering for Gene Expression Yoseph Barash
• Class Discovery in Gene Expression Data Amir Ben-Dor
• Likelihood Computations Using Value Abstraction Nir Friedman
• Learning Probabilistic Relational Models Nir Friedman
• On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks
• Belief Revision with Unreliable Observations Craig Boutilier
• Structured Representation of Complex Stochastic Systems Nir Friedman
• Efficient Bayesian Parameter Estimation in Large Discrete Domains
• Context-Specific Independence in Bayesian Networks Craig Boutilier
• Learning Bayesian Networks with Local Structure Nir Friedman
• A Qualitative Markov Assumption and Its Implications for Belief Change Nir Friedman
• First-Order Conditional Logic Revisited Nir Friedman
• Plausibility Measures: A User's Guide Nir Friedman
• Overabundance Analysis and Class Discovery in Gene Expression Data
• Nondeterministic Actions and the Frame Problem Craig Boutilier
• Understanding Protein-protein Interaction Networks
• Mean Field Variational Approximations in Continuous-Time Markov Processes
• From DNA Sequence to Chromatin Dynamics: Computational Analysis of Transcriptional
• Nucleosome Positioning from Tiling Microarray Data
• UNIFIED MODELS FOR REGULATORY Thesis submitted for the degree
• Towards an Integrated Protein-protein Interaction Map
• A Structural EM Algorithm for Phylogenetic Inference Matan Ninio
• Probabilistic Graphical Models in Systems Biology
• Graphical Models in Computational Molecular
• Learning Bayesian Networks from Data --NIPS 2001 Tutorial--
• The Information Bottleneck EM Algorithm Gal Elidan and Nir Friedman
• Continuous-Time Belief Propagation Tal El-Hay1
• Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm
• Towards an Integrated Protein-protein Interaction Ariel Jaimovich1,2
• Learning Bayesian Networks from Data ---AAAI 1998 Tutorial---
• Modeling Belief in Dynamic Systems. Part II: Revision and Update \Lambda
• Efficient Bayesian Parameter Estimation in Large Discrete Domains
• Belief Revision: A Critique Nir Friedman
• Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting