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Approximate Class Schedule

Wed 23 Course Presentation, Simulations of Discrete Distributions 1.1
Thu 24 Discrete Probability Distributions 1.2
Fri 25 Some problems: de Méré's problem, St Petersburg.  
Mon 28 Simulation of Continuous Distributions 2.1
Tue 29 Continuous density functions 2.2
Wed 30 Combinatorics : permutations 3.1
Thu 1 Section - Problems with equally likely outcomes.  
Fri 2 Problem Session -The birthday problem.  
Mon 5 Combinations, binomial coefiicients. 3.2
Tue 6 Binomial Distribution 3.2
Wed 7 Discrete Conditional Probability 4.1
Thu 8 Section - Inclusion -Exclusion Principle and Binomial.  
Fri 9 Problem Session - The Matching problem.  
Mon 12 Independence and Joint Probability Distributions. 4.1
Tue 13 Bayes Formula. 4.1
Wed 14 Continuous case of conditional probability. 4.2
Thu 15 Section - False positives.  
Fri 16 Problem Session - The Monty Hall Problem.  
Mon 19 Independence 4.2
Tue 20 Joint continuous distributions, Beta density. 4.2
Wed 21 Paradoxes. 4.3
Thu 22 Revisions for Midterm.  
Fri 23 First Midterm Exam.  
Mon 26 Discrete Distributions: uniform, geometric, negative binomial 5.1
Tue 27 Poisson Distribution. 5.1
Wed 28 Hypergeometric distribution.  
Thu 29 Section - Mule kicks and bombs.  
Fri 30 Buffon's needle.  
Mon 2 Exponential and Gamma 5.2
Tue 3 Normal Density 5.2
Wed 4 Expected Value, conditional expected value. 6.1
Thu 5 Section - Chi-square density  
Fri 6 Problem Session - Pascal and Stock Prices.  
Mon 9 Variance of discrete random variables. 6.2
Tue 10 Expected value of continuous random variables. 6.3
Wed 11 Variance of continuous random variables. 6.3
Thu 12 Revisions for Midterm.  
Fri 13 Second Midterm Exam.  
Mon 16 Sums of discrete random variables. 7.1
Tue 17 Sums of continuous random variables. 7.2
Wed 18 Laws of large numbers. 8.1
Thu 19 Section: Using indicator functions.  
Fri 20 Problem session - Covariance and Independence.  
Mon 23 Approximation of normal to the Binomial. 9.1
Tue 24 Central Limit theorem for sums of discrete iid rv's. 9.2
Wed 25 Overbooking and other applications of the clt.  
Thu 26 THANKSGIVING  
Fri 27 THANKSGIVING  
Mon 30 Central Limit for sums of iid continuous rv's.  
Tue 1 Examples of non -iid random variables: Markov Chains  
Wed 2 Applications : random walks.  
Thu 3 Section - Order statistics.  
Fri 4 Complete Review.  
Wed 9 FINAL EXAM -no make-up  


next up previous index
Next: Prerequisites Up: Logistics Previous: Office Hours
Susan Holmes
1998-12-07