PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS.
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 3: LINEAR MODELS FOR REGRESSION.
1 Multi-Choice Models. 2 Introduction In this section, we examine models with more than 2 possible choices Examples –How to get to work (bus, car, subway,
Trustworthy Service Selection and Composition CHUNG-WEI HANG MUNINDAR P. Singh A. Moini.
What attributes do Uganda pig traders consider to maximize returns and utility?: A choice experiment application
Dynamics of Social Interaction Frequency: Role of Geography and Accessibility
1 - Linear Regression
America CAS Seminar on Ratemaking March 2005 Presented by: Serhat Guven An Introduction to GLM Theory Refinements.
CS 59000 Statistical Machine learning Lecture 24 Yuan (Alan) Qi Purdue CS Nov. 20 2008.
The General Linear Model. The Simple Linear Model Linear Regression.
The max log likellihood function is simply a function of the error covariance matrix + constant terms!
A gentle introduction to Gaussian distribution. Review Random variable Coin flip experiment X = 0X = 1 X: Random variable.
