Learning Process Models with Missing Data Will Bridewell, Pat Langley, Steve Racunas, and Stuart Borrett Computational Learning Laboratory, CSLI, Stanford University, Stanford, CAUSA {willb,langley,sracunas,sborre

Source URL: cll.stanford.edu

A MRF Model Based Scheme for Accurate Detection and Adaptive Interpolation of Missing Data in Highly Corrupted Image Sequences Showbhik Kaka*, Dilip Krishnan* and M. N. Chong’ *Centrefor Signal Processing Nanyang Techn

Source URL: cs.nyu.edu

IEEE SIGNAL PROCESSING LETTERS, VOL. 5, NO. 4, APRILAn Edge-Preserving MRF Model for the Detection of Missing Data in Image Sequences

Source URL: cs.nyu.edu

Motion Segmentation with Missing Data using PowerFactorization and GPCA 1 Ren´e Vidal1,2 Center for Imaging Science Johns Hopkins University

Source URL: www.cis.jhu.edu

The data revolution: finding the missing millions - - Research reports and studies

Source URL: www.odi.org

doi:Distributed Selection: A Missing Piece of Data Aggregation By Fabian Kuhn, Thomas Locher, and Roger Wattenhofer

Source URL: www.tik.ee.ethz.ch

Data Compression, 4th Edition. Program and Pseudo-Code Listings (Advise the author about missing or bad listings.) Chapter 1 % Returns the run lengths of % a matrix of 0s and 1s

Source URL: www.davidsalomon.name

CSC Release 1: Missing sources in crowded fields Jonathan McDowell and Amy Mossman 2008 Sep 16 (Rev 1) During the course of CSC data review it was reported that some clearly visible sources were being missed. This note r

Source URL: cxc.cfa.harvard.edu

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing∗ , Jakob H. Macke∗,† , Maneesh Sahani Gatsby Computational Neuroscience Unit

Source URL: www.mackelab.org

• Statistics
• Statistical theory
• Estimation theory
• Actuarial science
• Expectationmaximization algorithm
• Missing data
• Maximum likelihood estimation
• Variational Bayesian methods
• Linear regression
• Normal distribution
• Covariance
• Regression analysis

Statistical analysis with missing data using multiple imputation and inverse probability weighting Training Course - University of St Andrews 20th – 22nd June 2011 This course aims to:

Source URL: www.scot-ship.ac.uk

• Statistics
• Statistical analysis
• Knowledge
• Information
• Missing data
• Survey methodology
• Imputation
• Inverse probability weighting
• Stata
• Weighting