Continuous Random Variable: Difference between revisions
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Continuous random variables have an inifinite number of values for any | |||
given interval. While similar, the approach to analysis is very | |||
different from discrete variables | |||
* Summation becomes integration | |||
* Probability becomes area under a curve | |||
= Probability Distribution Function <math> f(x) </math> = | |||
The probability density function (pdf) maps a continuous variable to a | |||
probability density. As the name "density" suggests, the area under the | |||
pdf curve between a range is the probability of the variable being in | |||
that range. | |||
= Uniform Distribution <math> X \sim Uniform(a, b) </math> = | = Uniform Distribution <math> X \sim Uniform(a, b) </math> = | ||
<math> a </math> is minimum, and <math> b </math> | |||
Revision as of 07:26, 1 March 2024
Continuous random variables have an inifinite number of values for any given interval. While similar, the approach to analysis is very different from discrete variables
- Summation becomes integration
- Probability becomes area under a curve
Probability Distribution Function
The probability density function (pdf) maps a continuous variable to a probability density. As the name "density" suggests, the area under the pdf curve between a range is the probability of the variable being in that range.
Uniform Distribution
is minimum, and
