# UAI 2013 - Subject Areas

When an author submits a paper, they will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. The terms have been grouped to provide a somewhat systematic overview of topics relevant to the UAI conference. For example, a paper about a new approximate inference algorithm for dynamic Bayesian network with applications to a problem in biology could select the combination *primary = dynamic Bayesian network, secondary = [application/biology, algorithms/approximate inference]* and so on.

For reference, below is the list of subject areas that will appear to authors and reviewers in the CMT conference management system:

Algorithms: Approximate Inference |

Algorithms: Belief Propagation |

Algorithms: Distributed and Parallel |

Algorithms: Exact Inference |

Algorithms: Graph Theory |

Algorithms: Heuristics |

Algorithms: MCMC methods |

Algorithms: Optimization |

Algorithms: Other |

Algorithms: Software and Tools |

Applications: Biology |

Applications: Databases |

Applications: Decision Support |

Applications: Diagnosis and Reliability |

Applications: Economics |

Applications: General |

Applications: Medicine |

Applications: Planning and Control |

Applications: Privacy and Security |

Applications: Robotics |

Applications: Sensor Data |

Applications: Social Network Analysis |

Applications: Speech |

Applications: Sustainability and Climate |

Applications: Text and Web Data |

Applications: User Models |

Applications: Vision |

Data: Multivariate |

Data: Other |

Data: Relational |

Data: Spatial |

Data: Temporal or Sequential |

Learning: Active Learning |

Learning: Classification |

Learning: Clustering |

Learning: Deep Learning |

Learning: General |

Learning: Nonparametric Bayes |

Learning: Online and Anytime Learning |

Learning: Other |

Learning: Parameter Estimation |

Learning: Probabilistic Generative Models |

Learning: Ranking |

Learning: Recommender Systems |

Learning: Regression |

Learning: Reinforcement Learning |

Learning: Relational Learning |

Learning: Scalability |

Learning: Semi-Supervised Learning |

Learning: Structure Learning |

Learning: Structured Prediction |

Learning: Theory |

Learning: Unsupervised |

Methodology: Bayesian Methods |

Methodology: Calibration |

Methodology: Elicitation |

Methodology: Evaluation |

Methodology: Human Expertise and Judgement |

Methodology: Other |

Methodology: Probabilistic Programming |

Models: Bayesian Networks |

Models: Directed Graphical Models |

Models: Dynamic Bayesian Networks |

Models: Markov Decision Processes |

Models: Mixed Graphical Models |

Models: Other |

Models: Undirected Graphical Models |

Principles: Causality |

Principles: Cognitive Models |

Principles: Decision Theory |

Principles: Game Theory |

Principles: Information Theory |

Principles: Other |

Principles: Probability Theory |

Principles: Statistical Theory |

Representation: Constraints |

Representation: Dempster-Shafer |

Representation: Fuzzy Logic |

Representation: Influence Diagrams |

Representation: Non-Probabilistic Frameworks |

Representation: Probabilistic |