Class summary:
Wednesday, 3/19
I started with Sections 3.4 and 3.5.
[Section 3.3, on normal approximation,
will be covered after the break.]
These sections do not introduce major new concepts or results, but
illustrate the techniques from 3.1 and 3.2 for computing probabilities
involving random variables in the case these random variables have a
"named" distribution, such as the geometric or the Poisson
distributions. I reviewed the definitions of these distributions and
associated formulas (the geometric series in the case of the geometric
distribution, and the exponential series for the Poisson
distribution). I illustrated working with these distributions by
computing the expectation of a geometric(p) distribution (this is hard
and requires a special trick), the expectation of the Poisson(mu)
distribution (a routine exercise). I also did an example of a
probability computation involving the geometric distribution
(two players keep tossing a coin until one of the players gets a head;
what is the probability that a tie occurs i.e., that both get
the first head at the same time?).
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