'Necessarily independent' presentation slideshows

Expectancy-Value Model For Market Share Prediction and Price Setting

Expectancy-Value Model For Market Share Prediction and Price Setting. Ted Mitchell. Your Job is to Predict . 1) Which Brand the customer will buy? 2) What is the Maximum Price the customer will pay for it? 3) What your relative Market share should be?

657 views • 57 slides

Randomized Algorithms

Randomized Algorithms. Randomization. Algorithmic design patterns. Greed. Divide-and-conquer. Dynamic programming. Network flow. Randomization. Randomization. Allow fair coin flip in unit time.

765 views • 55 slides

Subspace Clustering Algorithms and Applications for Computer Vision Amir Adler

Subspace Clustering Algorithms and Applications for Computer Vision Amir Adler. Agenda. The Subspace Clustering Problem Computer Vision Applications A Short Introduction to Spectral Clustering Algorithms Sparse Subspace Clustering (CVPR 2009) Low Rank Representation (ICML 2010)

620 views • 42 slides

In the following slides, you will be asked a series of questions.

423 views • 29 slides

Algorithm Design and Analysis

310 views • 23 slides

Observation Methods

Observation Methods. GLT: Evaluate participant, non-participant, naturalistic, overt and covert observations. What is an observation?. What do you think these terms might mean?. Participant Observation Non-participant Observation Naturalistic Observation Covert Observation

778 views • 9 slides

Definition of Covariance

Definition of Covariance. The covariance of X & Y , denoted Cov ( X , Y ), is the number where m X = E ( X ) and m Y = E ( Y ). Computational Formula:. Variance of a Sum. Covariance and Independence. If X & Y are independent, then Cov ( X , Y ) = 0.

1.03k views • 8 slides

Definition of Covariance

Definition of Covariance. The covariance of X & Y , denoted Cov ( X , Y ), is the number where m X = E ( X ) and m Y = E ( Y ). Computational Formula:. Variance of a Sum. Covariance and Independence. If X & Y are independent, then Cov ( X , Y ) = 0.

826 views • 8 slides