JMLR: Workshop and Conference Proceedings vol 40:1–27, 2015 Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification Dehua Cheng1 Yu Cheng1

Source URL: jmlr.org

Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

Source URL: www.cs.toronto.edu

• Statistics
• Probability
• Statistical theory
• Probability distributions
• Statistical models
• Bayesian statistics
• Stochastic processes
• Graphical models
• Bayesian network
• Gibbs sampling
• Dirichlet process
• Mixture model

Mean Field Variational Approximations in Continuous-Time Markov Processes A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

Source URL: www.cs.huji.ac.il

• Bayesian statistics
• Statistics
• Graphical models
• Probability
• Markov models
• Bayesian network
• Probability distribution
• Markov chain
• Expectation propagation
• Approximate inference
• Likelihood function
• Hidden Markov model

h0p://www.cs.umd.edu/linqs   Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen H. Bach, Matthias Broecheler, Lise Getoor, and Dianne P. O’Leary

Source URL: psl.umiacs.umd.edu

• Graphical models
• Markov random field
• Probability theory

Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g

Source URL: psl.umiacs.umd.edu

• Artificial intelligence
• Probability
• Statistics
• Bayesian statistics
• Markov networks
• Graphical models
• Markov random field
• Probability theory
• Image segmentation
• Probabilistic soft logic
• Activity recognition
• Support vector machine

Spheres of Influence for More Effective Viral Marketing Yasir Mehmood Francesco Bonchi David García-Soriano

Source URL: www.francescobonchi.com

• Mathematics
• Graph theory
• Computational complexity theory
• Approximation algorithm
• Randomized algorithm
• Networks
• Graphical models
• Centrality
• Belief propagation

Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies Stephen H. Bach Computer Science Dept. University of Maryland

Source URL: stephenbach.net

• Graphical models
• Mathematical analysis
• Mathematics
• Probability
• Mathematical optimization
• Operations research
• Linear programming
• Probability theory
• Markov random field
• Linear programming relaxation
• Relaxation
• Bayesian network

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic arXiv:1505.04406v2 [cs.LG] 9 DecStephen H. Bach∗ Matthias Broecheler† Bert Huang‡ Lise Getoor§

Source URL: stephenbach.net

• Logic
• Graphical models
• Mathematics
• Mathematical logic
• Structured prediction
• Markov random field
• Probability theory
• Non-classical logic
• Logic in computer science
• Bayesian network
• Random field
• Fuzzy logic

Journal of Artificial Intelligence Research–774 Submitted 03/10; publishedIntrusion Detection using Continuous Time Bayesian Networks Jing Xu

Source URL: rlair.cs.ucr.edu

• Statistics
• Estimation theory
• Control theory
• Mathematical analysis
• Markov models
• Graphical models
• Bayesian network
• Maximum likelihood estimation
• Expectationmaximization algorithm
• Markov chain
• Discretization
• State-space representation

Genome Informatics 11: 129–Cluster Inference Methods and Graphical Models Evaluated on NCI60 Microarray Gene Expression Data

Source URL: www.jsbi.org

• Biology
• Biochemistry
• Cell biology
• Systems biology
• Gene expression
• Estimation theory
• Parametric statistics
• Regression analysis
• Gene regulatory network
• P53
• Cluster analysis
• Ordinary least squares