SUPPLIER CODE OF CONDUCT This Supplier Code of Conduct (“Code”) sets forth Calpine Corporation’s (including its subsidiaries and affiliates, hereinafter “Calpine”) expectation of suppliers, vendors, contractors

Source URL: www.calpine.com

C HRISTIAN R. S HELTON University of California, Riverside Department of Computer Science & Engineering 327 Chung Hall Riverside, CA 92521

Source URL: www.cs.ucr.edu

• Computational statistics
• Expectation propagation
• International Conference on Machine Learning
• Conference on Neural Information Processing Systems
• Daphne Koller
• Ambient intelligence
• Machine learning
• Artificial intelligence
• Learning

LATENT CLASS ANALYSIS WITH SAMPLING WEIGHTS: A MAXIMUM LIKELIHOOD APPROACH Jeroen K. Vermunt Department of Methodology and Statistics, Tilburg University Jay Magidson Statistical Innovations Inc.

Source URL: www.statisticalinnovations.com

• Mathematical analysis
• Expectation–maximization algorithm
• Missing data
• Mixture model
• Latent class model
• Latent variable model
• Weight function
• Maximum likelihood
• Statistics
• Estimation theory
• Statistical models

A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data: Supplementary Material Do-kyum Kim, Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering, Univers

Source URL: cseweb.ucsd.edu

• Data analysis
• Probability theory
• Expectation–maximization algorithm
• Variance
• Statistics
• Estimation theory
• Central limit theorem

A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering, University of California, San D

Source URL: cseweb.ucsd.edu

• Estimation theory
• Learning
• Statistical theory
• Semi-supervised learning
• Expectation–maximization algorithm
• Supervised learning
• Support vector machine
• Maximum likelihood
• Stability
• Statistics
• Machine learning
• Statistical classification

Sequential Monte Carlo for Simultaneous Passive Device-Free Tracking and Sensor Localization Using Received Signal Strength Measurements Xi Chen Andrea Edelstein

Source URL: www.tsp.ece.mcgill.ca

• Control theory
• Robot control
• Computational statistics
• Monte Carlo methods
• Linear filters
• Kalman filter
• Particle filter
• Expectation–maximization algorithm
• Auxiliary particle filter
• Statistics
• Technology
• Estimation theory

Bayesian Adaptive Exploration Tom Loredo & David Chernoff Department of Astronomy, Cornell University http://www.astro.cornell.edu/staff/loredo/bayes/

Source URL: www.astro.cornell.edu

• Estimation theory
• Bayesian inference
• Bayes estimator
• Loss function
• Bayesian experimental design
• Expectation–maximization algorithm
• Kullback–Leibler divergence
• Statistics
• Bayesian statistics
• Statistical theory

One-step sparse estimates in nonconcave penalized likelihood modelsT1

Source URL: www.personal.psu.edu

• Econometrics
• Parametric statistics
• Statistical theory
• Linear regression
• Expectation–maximization algorithm
• Ordinary least squares
• Stability
• Maximum likelihood
• Normal distribution
• Statistics
• Regression analysis
• Estimation theory

The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models

Source URL: www.stat.washington.edu

• Operations research
• Machine learning
• Estimation theory
• Expectation–maximization algorithm
• Missing data
• Constraint algorithm
• Mathematical optimization
• Algorithm
• XTR
• Mathematics
• Applied mathematics
• Statistics

Promoting good practice and the expectation of good practice in mentoring and coaching across Europe December

Source URL: www.emccouncil.org