PPT-NEW MODELS

FROM OLD TOOLS CAROL L TILLEY and KATHRYN LA BARRE Graduate School of Library and Information Science University of Illinois ISKO 2010 Partial funding provided by an OCLCALISE LISR Program Grant. 41 Ellesmere Po New New Hig New Eas New 15 mins 30 mins 15 mins 30 mins 60 mins 30 mins 20 mins 10 mins 30 mins 218219 30 mins 15 mins 30 mins 15 mins 30 mins 60 mins 30 mins 20 mins 20 mins 30 mins 5735557347573595734757347kml573475735957347Ikfbk57347Lkbl57347BgeZrl S S57347Ibdnil573475735957347OheMhgPZr573475735957347khok 57347Mngkl S57347Lmhi57347MZbe Bolero G 573475737557347 O57347FZahZgrFZie573475735957347JF57347FZahZgrJnbem57347FZie5734757 Linear models are easier to understand than nonlinear models and are necessary for most contro l system design methods brPage 2br Single Variable Example A general single variable nonlinear model The function can be approximated by a Taylor seri The ARMApq series is generated by 12 pt pt 12 qt 949 949 949 Thus is essentially the sum of an autoregression on past values of and a moving average o tt t white noise process Given together with starting values of the whole series Eigenvalues. (9.1) Leslie Matrix Models. (9.2) Long Term Growth Rate (. Eigenvalues. ). (9.3) Long Term Population Structure (Corresponding Eigenvectors). Introduction. In the models presented and discussed in Chapters 6, 7, and 8, nothing is created or destroyed:. CMSC 723: Computational Linguistics I ― Session #9. Jimmy Lin. The . iSchool. University of Maryland. Wednesday, October 28, 2009. N-Gram Language Models. What? . LMs assign probabilities to sequences of tokens. Martin Goldberg, Executive Director. Clearing Compliance and Risk Management. CME Group. martin.goldberg@cmegroup.com. The Usual Caveats. This course expresses my own personal opinions and may not represent the views of any past, present, or future employers. It may conflict with your views. Feel free to disagree.. Michael . Massoglia. Department of Sociology. University of Wisconsin Madison . General Overview. The logic of propensity models. Application based discussion of some of the key features . Emphasis on working understanding use of models . Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. Instructor: Paul Tarau, based on . Rada. . Mihalcea’s. original slides. Note. : some of the material in this slide set was adapted from an NLP course taught by Bonnie Dorr at Univ. of Maryland. Language Models. Lecture 06. Thomas Herring. tah@mit.edu. . Issues in GPS Error Analysis. What are the sources of the errors ?. How much of the error can we remove by better modeling ?. Do we have enough information to infer the uncertainties from the data ?. Arie Gurfinkel (SEI/CMU) with. Marsha . Chechik. (Univ. of Toronto). Shoham. Ben-David (Univ. of Toronto), Sebastian . Uchitel. (Univ. of Buenos Aires and Imperial College London). Position. Models for. Count Data. Doctor Visits. Basic Model for Counts of Events. E.g., Visits to site, number of purchases, number of doctor visits. Regression approach. Quantitative outcome measured. Discrete variable, model probabilities.