Details of our Motif Clustering Procedure 1 Bayesian Hierarchical Clustering Model The data for each discovered motif is a count matrix Ni which can have different widths and number of counts compared to other TF motifs.

Source URL: www-stat.wharton.upenn.edu

• Hierarchical clustering
• Network analysis
• Cluster analysis
• Probability distributions

Context-Sensitive Induction Patrick Shafto1 , Charles Kemp1 , Elizabeth Baraff1 , John D. Coley2 , & Joshua B. Tenenbaum1 1 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology 2

Source URL: ccdlab.rutgers.edu

• Reasoning
• Inductive reasoning
• Philosophy of science
• Statistical inference
• Critical thinking
• Causality
• Concept learning
• Bayesian network
• Inference
• Bayesian inference
• Theory
• Argument

326 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 3, MARCH 2008 Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

Source URL: decsai.ugr.es

• Statistics
• Statistical theory
• Bayesian statistics
• Conjugate prior
• Hyperprior
• Normal distribution
• Bayesian inference
• Hyperparameter
• Bayesian network
• Prior probability
• Hidden Markov model
• Maximum likelihood estimation

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Source URL: cocosci.berkeley.edu

• Statistics
• Statistical theory
• Statistical inference
• Philosophy of science
• Bayesian statistics
• Bayesian network
• Bayesian inference
• Bayesian probability
• Decision theory
• Prior probability
• Concept learning
• Markov chain Monte Carlo

Is Holism A Problem For Inductive Inference? A Computational Analysis Maxwell A. Bertolero () Thomas L. Griffiths (tom ) Department of Psychology & Helen Wills Neuroscience Ins

Source URL: cocosci.berkeley.edu

• Network theory
• Graph theory
• Networks
• Statistical models
• Algebraic graph theory
• Community structure
• Modularity
• Connectivity
• Bayesian network
• Graphical model
• Graph
• Clique

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Source URL: cocosci.berkeley.edu

• Statistics
• Statistical theory
• Estimation theory
• Probability distributions
• Regression analysis
• Parametric statistics
• Normal distribution
• Bayesian network
• Linear regression
• Bayesian inference
• Variance
• Mode

Cognition–223 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT

Source URL: eccl.mit.edu

• Philosophy
• Statistics
• Metaphysics
• Causality
• Philosophy of science
• Graphical models
• Causal inference
• Conditionals
• Causal reasoning
• Causal model
• Bayesian network
• Determinism

Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia1 , Joshua Abbott2 , Joseph Austerweil3 , Thomas Griffiths2 , Trevor Darrell1 1 UC Berkeley EECS 2 Dept of

Source URL: cocosci.berkeley.edu

• Statistics
• Artificial intelligence
• Machine learning
• Cognition
• Computational neuroscience
• Concept learning
• Learning theory
• ImageNet
• Artificial neural network
• Supervised learning
• Support vector machine
• Probability distribution

A Non-parametric Bayesian Network Prior of Human Pose Andreas M. Lehrmann MPI for Intelligent Systems Tuebingen, Germany Peter V. Gehler

Source URL: ps.is.tuebingen.mpg.de

Statistics 542 Spring 2016 Course Description: Bayesian Methods and Computation The goal of this course is to develop sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Key topi

Source URL: www-stat.wharton.upenn.edu

• Statistics
• Statistical theory
• Statistical inference
• Statistical models
• Sampling techniques
• Statistical mechanics
• Stochastic simulation
• Bayesian network
• Bayesian inference
• Bayesian statistics
• Bayesian
• Monte Carlo method