Church: a language for generative models Noah D. Goodman, Vikash K. Mansinghka∗, Daniel M. Roy, Keith Bonawitz & Joshua B. Tenenbaum MIT BCS/CSAIL Cambridge, MA 02139

Source URL: stanford.edu

• Software engineering
• Computer programming
• Computing
• Lambda calculus
• Functional programming
• Programming languages
• Data types
• Control flow
• Eval
• Bayesian network
• Scheme
• Lisp

Learning Rules from Incomplete Examples via Observation Models Janardhan Rao Doppa, Mohammad NasrEsfahani, Mohammad S. Sorower, Jed Irvine Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli School of EECS, Oregon St

Source URL: web.engr.oregonstate.edu

• Applied mathematics
• Probability and statistics
• Statistics
• Bayesian network
• Scoring rule
• Machine learning
• Confidence interval
• Expectationmaximization algorithm
• Semi-supervised learning

Exploiting Task-Based Parallelism in Bayesian Uncertainty Quantification Panagiotis E. Hadjidoukas1(B) , Panagiotis Angelikopoulos1 , Lina Kulakova1 , Costas Papadimitriou2 , and Petros Koumoutsakos1 1

Source URL: www.cse-lab.ethz.ch

• Statistics
• Statistical theory
• Bayesian statistics
• Estimation theory
• Parallel computing
• Bayesian network
• Bayesian inference
• Loss function
• Likelihood function
• Uncertainty quantification
• Markov chain Monte Carlo
• Marginal likelihood

Learning Bayesian Network Structure using LP Relaxations Tommi Jaakkola MIT CSAIL David Sontag

Source URL: cs.nyu.edu

A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models Brendan J. Frey and Nebojsa Jojic Abstract Computer vision is currently one of the most exciting areas of artificial intelligence re

Source URL: www.isit2004.org

• Statistics
• Statistical models
• Probability
• Graphical models
• Machine learning
• Statistical theory
• Bayesian statistics
• Markov random field
• Bayesian network
• Generative model
• Factor graph
• Discriminative model

Agglomerative Clustering of Bagged Data Using Joint Distributions David Arbour* James Atwood*

Source URL: kdl.cs.umass.edu

• Statistical theory
• Model selection
• Networks
• Cluster analysis
• Data mining
• Geostatistics
• Hierarchical clustering
• Bayesian network
• KullbackLeibler divergence
• Akaike information criterion
• Entropy
• Bayesian information criterion

A Stochastic Model for Reputation Management in Introduction-Based Trust Systems1,2 O. Patrick Kreidl University of North Florida Jacksonville, Florida, U.S.A.

Source URL: stoch-nets-2012.lids.mit.edu

• Computing
• Transmission Control Protocol
• Bayesian network
• Routing
• Tree

Advanced Review Bayesian Vertex Nomination Using Content and Context Shakira Suwan,1* Dominic S. Lee1 and Carey E. Priebe2 Using attributed graphs to model network data has become an attractive approach

Source URL: www.cis.jhu.edu

On Trusting Introductions from a Reputable Source: A Utility-Maximizing Probabilistic Approach† Richard Al-Bayaty∗ , Patrick Caldwell and O. Patrick Kreidl School of Engineering, University of North Florida, Jacksonv

Source URL: www.unf.edu

• Graphical models
• Bayesian network
• PL

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data Guy Van den Broeck∗ and Karthika Mohan∗ and Arthur Choi and Adnan Darwiche and Judea Pearl Computer Science Department University of C

Source URL: lirias.kuleuven.be