CAAD FUTURES DIGITAL PROCEEDINGSChapter 9

Source URL: cs.gmu.edu

• Software engineering
• Computing
• Computer programming
• Reasoning
• Artificial intelligence
• Expert systems
• Programming paradigms
• Prolog
• Cognitive architecture
• Computer-aided architectural design
• Logic programming
• Inference

2015 IEEE International Conference on Robotics and Automation (ICRA) Washington State Convention Center Seattle, Washington, May 26-30, 2015 Self-tuning M-estimators G. Agamennoni, P. Furgale and R. Siegwart

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• Estimation theory
• Statistical theory
• M-estimators
• Statistical inference
• Bayesian statistics
• Maximum likelihood estimation
• Normal distribution
• Bayes estimator
• Efficiency
• Estimator
• Marginal likelihood
• operator

Static Inference of Universe Types Ana Milanova Rensselaer Polytechnic Institute Abstract

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J. R. Statist. Soc. B, Part 2, pp. Inference for linear models with dependent errors Zhou Zhou University of Toronto, Canada

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REV_ISS_WEB_COGS_12101_38

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• 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

Practical Static Analysis for Inference of Security-Related Program Properties Yin Liu Department of Computer Science Rensselaer Polytechnic Institute

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Experience from Measuring Program Comprehension—Toward a General Framework Janet Siegmund∗, Christian K¨astner, Sven Apel, Andr´e Brechmann, Gunter Saake Abstract: Program comprehension plays a crucial role during

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• Software engineering
• Computing
• Software
• Causal inference
• Design of experiments
• Educational psychology
• Applied linguistics
• Orthography
• Object Process Methodology
• Confounding
• Program comprehension
• Experiment

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola1 , Fredrik Lindsten2 , Thomas B. Sch¨on2,3 and Carl E. Rasmussen1 1. Dept. of Engineering, University of Cambridge,

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

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

Subvector Inference in Local Regression Ke-Li Xuy Texas A&M University December 3, 2014 Abstract

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