PPT-Applications of the Canonical Ensemble

Simple Models of Paramagnetism First a Quantum System of Spins s ½ Paramagnetic Materials The following is a simple model to develop our understanding of Statistical Mechanics in . e AT where is called a Jordan block of size with eigenvalue so 1 Jordan canonical form 122 brPage 3br is upper bidiagonal diagonal is the special case of Jordan blocks of size 1 Jordan form is unique up to permutations of the blocks can have multipl for the NCEP GFS. Tom Hamill, for . Jeff . Whitaker. NOAA Earth System Research Lab, Boulder, CO, USA. jeffrey.s.whitaker@noaa.gov. Daryl Kleist, Dave Parrish and John . Derber. National Centers for Environmental Prediction, Camp Springs, MD, USA. fundamentals. Tom Hamill. NOAA ESRL, Physical Sciences Division. tom.hamill@noaa.gov. NOAA Earth System. Research Laboratory. “Ensemble weather prediction”. possibly. different. models. or models. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Are we still talking about diversity in classifier ensembles?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Kalman. Filters. Yun Liu. Dept. of Atmospheric and Oceanic . Science, University of Maryland  . Atmospheric and oceanic . s. ciences and Center for Climatic . R. esearch, UW-Madison. Collaborators: X. . and post-processing . team reports to NGGPS. Tom Hamill. ESRL, Physical Sciences Division. tom.hamill@noaa.gov. (303) 497-3060. 1. Proposed team . members. Ensemble system development. Post-processing. Canonical Correlation/Regression. AKA multiple, multiple regression. AKA multivariate multiple regression. Have two sets of variables (. Xs. and Ys). Create a pair of canonical . variates. . a. 1. X. Molly Smith, Ryan Torn, . Kristen . Corbosiero. , and Philip . Pegion. NWS Focal Points: . Steve . DiRienzo. . and Mike . Jurewicz. . WFO . BGM Sub-Regional Workshop . 23 September, 2015. Motivation. Which of the two options increases your chances of having a good grade on the exam? . Solving the test individually. Solving the test in groups. Why?. Ensemble Learning. Weak classifier A. Ensemble Learning. Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . Better Predictions Through Diversity. Todd Holloway. ETech 2008. Outline. Building a classifier (a tutorial example). Neighbor method. Major ideas and challenges in classification. Ensembles in practice. Modeling and Development Division. CPTEC/INPE. Middle-Range Ensemble Forecast at CPTEC/INPE - Current Activities. 2. Local Ensemble Transformed . Kalman. Filter. OUTLINE. 3. New method to obtain perturbed initial conditions . Craig H. Bishop. The University of Melbourne, Parkville, Australia. Joanna (Asia) S. Pelc. Selina, Medellin, Columbia. With Acknowledgements to . Sergey . Frolov. , Doug Allen, Rolf Langland, Karl . . Asish Ghoshal. 1. , . Ananth. . Grama. 1. , . Saurabh. Bagchi. 2. , . Somali . Chaterji. 1. . Purdue University, West Lafayette, IN. . MicroRNA (miRNA): “The genome’s guiding hand?”. miRNA are 22 nucleotide .