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An introduction to probability theory, focusing on bernoulli, binomial, geometric, and poisson distributions. Students will learn how to build models, compute probabilities, and understand the physical phenomena these distributions represent. Commonly used probability models are discussed, including their names, parameters, and applications.
What you will learn
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counting
sample space^ are equally likely
continnons with finite integra
for a^ given model
Bayes Rule compute summary^
expected value^ variance moments
3.5 (^) and (^) 4. There are^ some^ pints^ and
discrete and continuous^ RVs^ that^
over and^ over To (^) make it^ easier^ to (^) communicate about^ them
a name Many are also^ easily described by
probability
the name and (^) the parameters
over and^ over^ because (^) they are
phenomenon
Continuous (^) pdfs
μ o Binomial (^) Mp
2 Geometric p^ Uniform (^) a b Uniform Ca^ b^
Poisson a (^) Cauchy Zipf s
binomial Ise can^ be^ looked^ up^ BUT (^) you'll need^ to
Applications
Bernoulli
received
send N^ packets^ How many packets received out of^ N
have (^) defects Geometric
until it's^ received How many
you send (^) the (^) packet test (^) chips until^ you find a^ defective^ one How
many
with a^ small^
of loss How^ many
received out of^ many N (^) chips with^
chance of defeat^ how^ many^
also useful to measure^ things
period or in^ a spatial region In all^ cases^ think^ carefully
underlying event (^) A and the (^) meaning of p P^ A^ and of^ the^ variable^ X
