To find out more about what the version numbers mean, click
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Conditional probability as applied to any
application with transition probabilities, and how it is used
in detection/decision theory.
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This
module is about continuous random variables, specifically
the Uniform, Normal (Gaussian), and Exponential families of
random variables. By completing this module, you will learn
more about these random variables, including their probability
density functions, cumulative density functions, and how the
samples from the distributions are used to estimate the population
(or theoretical) distribution. In addition, certain real-life
applications of these distributions will be introduced.
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Most
businesses -- retailers, wholesalers, manufacturers -- need
to keep a supply of goods readily available for sale or use.
Generally, companies want to keep as little capital as possible
tied up in inventory, but at the same time they don't want
to incur losses (in profits, customer base, or reputation)
by not being able to meet demand. The question then
becomes: How do companies decide when to order new stock and
how much to order? This module introduces one methodology
for inventory control : the Economic Order Quantity Model.
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The mean and the variance of a random variable
are just two numbers that are the statistics of data or the
shape of the probability density function. In the context
of additive noise they determine how the resulting signal
appears and how well that 'noisy' signal can be used in the
application. This module explores the roles that the mean
and the variance play in the analysis of the application.
The applications that are used are digital images and binary
signal detection.
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Random
Variable Relations
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