IE 300, Fall 2012Richard Sowers
IESE
8/30/2012
Goals:•Rules of Probability•Counting•Equally likely•Some examples
9/4/2012
Goals•Conditional Probability•Model-building
9/6/2012
• Independence– Definition– Model-building– Examples
9/11/2012
• Bayes’ Rule– Implicit conditional probabilities
• Random variables (if possible)
9/13/2012
• Discrete random variables– Examples
• Uniform• Geometric• Binomial• Bernoulli
– Probability mass functions• Expectations
9/27/2012
• Expectations and variances– Direct computations– Moment generating functions (not in book)
9/18/2012
• Expectations– Means– Variances– Moment generating functions
• Several random variables– Binomial– Geometric– Bernoulli– Poisson– Uniform
9/20/2012
• Review
9/28/2012
• Expectations and Variances– Bernoulli– Binomial– Law of large numbers
10/2/2012
• Moment generating function– Bernoulli– Binomial– Geometric– Negative Binomial– Poisson
• Begin continuous random variables
10/4/2012
• Continuous random variables• Properties of densities• Exponential random variables
10/9/2012
• Poisson connection to exponential random variables
• Gaussian random variables
10/11/2012
• Gaussian random variables• Limit theorems
10/18/2012
• Joint random variables• Joint probability mass function• Conditioning• Marginals
10/23/2012
• More joint random variables• Joint probability mass function• Conditioning• Marginals
11/1/2012
• More on transformations of random variables
• Joint Gaussians
11/6/2012
• Statistical analysis of data• Sample mean, sample variance, population
variance• Histograms and frequency plots
11/13/2012
• Estimation of parameters– Bernoulli– Exponential– Gaussian
11/15/2012
• Hypothesis testing (9-5)
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