PPT-Cloud Model Choice

Private Hybrid and LesserKnown Models for Partners Lee Bank Senior Director of Alliances amp Channels Oracle Financing January 2015 2 Creating Competitive Advantage Give partners the option to provide perpetual license rights at the end of the service term. William Greene. Stern School of Business. New York University. Part 7-1. Latent Class Models. Discrete Parameter Heterogeneity. Latent Classes. Latent Class Probabilities. Ambiguous – Classical Bayesian model?. A Microeconomics Platform. Consumers Maximize Utility . (!!!). Fundamental Choice Problem: Maximize U(x. 1. ,x. 2. ,…) subject to prices and budget constraints. A Crucial Result for the Classical Problem:. William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. Microeconomic Applications. University of Lugano, Switzerland. May 27-31, 2013. William Greene. Department of Economics. Stern School of Business. 2C. Multinomial Choice. Agenda for 2C. Random Utility. Exploring Information Leakage in Third-Party Compute Clouds. By Thomas Ristenpart et al.. Edward Wu. Structure. High Level Picture/Motivation. Thread Model. Approach. Mitigations. Pros/Cons. What's New/Not New in Cloud Security?. William Greene. Stern School of Business. New York University. Part 11. Modeling Heterogeneity. Several Types of Heterogeneity. Observational: Observable differences across. choice makers. Choice strategy: How consumers make. William Greene. Stern School of Business. New York University. Part 6. Modeling Heterogeneity. Several Types of Heterogeneity. Differences across choice makers. Observable: Usually demographics such as age, sex. MILENA RMU. Š. Master of Agriculture, Food and Environment Policy Analysis. Supervisors:. Zein. . Kallas. Jose Maria Gil. Introduction. Role of r. enewable energ. y sources. Directive 2009/28/EC on the promotion of use of energy from renewable sources - mandatory targets for MSs of 20% share of renewable energy in total energy consumption and 10% share of energy from renewable sources in all forms of transport by 2020.. MILENA RMU. Š. Master of Agriculture, Food and Environment Policy Analysis. Supervisors:. Zein. . Kallas. Jose Maria Gil. Introduction. Role of r. enewable energ. y sources. Directive 2009/28/EC on the promotion of use of energy from renewable sources - mandatory targets for MSs of 20% share of renewable energy in total energy consumption and 10% share of energy from renewable sources in all forms of transport by 2020.. William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. TRB Transportation Planning Applications 2011 . |. Reno, NV. Rick Donnelly & Tara Weidner. | . PB . |. [. donnellyr. , . weidner. ]@. pbworld.com. Overview. Concepts. Albuquerque HBW example (urban). William Greene. Stern School of Business. New York University. New York NY USA. 3. .3 . Discrete Choice; The. Multinomial Logit. Model. Concepts. Random Utility. Multinomial Choice. Discrete Choice Modeling William Greene Stern School of Business New York University Part 2 Estimating and Using Binary Choice Models Agenda A Basic Model for Binary Choice Specification Maximum Likelihood Estimation Platform:. Enabling Service Based Environmental . Modelling. Using Infrastructure. -as-a-Service . Cloud Computing. Olaf David. iEMSs. – Leipzig, Germany - July . 2012. olaf.david@colostate.edu. USDA .