| Line 1: | Line 1: | ||
{| | {| | ||
|- | |- | ||
| − | ! style="background: | + | ! style="background: rgb(228, 188, 126) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial; font-size: 110%;" colspan="2" | Probability Formulas |
|- | |- | ||
| − | ! style="background: | + | ! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Properties of Probability Functions |
|- | |- | ||
| align="right" style="padding-right: 1em;" | The complement of an event A (i.e. the event A not occurring) | | align="right" style="padding-right: 1em;" | The complement of an event A (i.e. the event A not occurring) | ||
| Line 27: | Line 27: | ||
| align="right" style="padding-right: 1em;" | Bayes Theorem | | align="right" style="padding-right: 1em;" | Bayes Theorem | ||
| <math>\,P(A_j|B) = \frac{P(B|A_j)P(A_j)}{\sum_{i=1}^{n}P(B|A_i)P(A_i)},\ \{A_i\} \mbox{ and } B \mbox{ are as above }.</math> | | <math>\,P(A_j|B) = \frac{P(B|A_j)P(A_j)}{\sum_{i=1}^{n}P(B|A_i)P(A_i)},\ \{A_i\} \mbox{ and } B \mbox{ are as above }.</math> | ||
| − | |||
| − | |||
| − | |||
|- | |- | ||
| − | ! style="background: | + | ! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Expectation and Variance of Random Variables |
|- | |- | ||
| align="right" style="padding-right: 1em;" | Binomial random variable with parameters n and p | | align="right" style="padding-right: 1em;" | Binomial random variable with parameters n and p | ||
Revision as of 10:43, 22 October 2010
| Probability Formulas | |
|---|---|
| Properties of Probability Functions | |
| The complement of an event A (i.e. the event A not occurring) | $ \,P(A^c) = 1 - P(A)\, $ |
| The intersection of two independent events A and B | $ \,P(A \mbox{ and }B) = P(A \cap B) = P(A) P(B)\, $ |
| The union of two events A and B (i.e. either A or B occurring) | $ \,P(A \mbox{ or } B) = P(A) + P(B) - P(A \mbox{ and } B)\, $ |
| The union of two mutually exclusive events A and B | $ \,P(A \mbox{ or } B) = P(A \cup B)= P(A) + P(B)\, $ |
| Event A occurs given that event B has occurred | $ \,P(A \mid B) = \frac{P(A \cap B)}{P(B)}\, $ |
| Total Probability Law | $ \,P(B) = P(B|A_1)P(A_1) + \dots + P(B|A_n)P(A_n)\, $
$ \mbox{ where } \{A_1,\dots,A_n\} \mbox{ is a partition of sample space } S, B \mbox{ is an event }. $
|
| Bayes Theorem | $ \,P(A_j|B) = \frac{P(B|A_j)P(A_j)}{\sum_{i=1}^{n}P(B|A_i)P(A_i)},\ \{A_i\} \mbox{ and } B \mbox{ are as above }. $ |
| Expectation and Variance of Random Variables | |
| Binomial random variable with parameters n and p | $ \,E[X] = np,\ \ Var(X) = np(1-p)\, $ |
| Poisson random variable with parameter $ \lambda $ | $ \,E[X] = \lambda,\ \ Var(X) = \lambda\, $ |
| Geometric random variable with parameter p | $ \,E[X] = \frac{1}{p},\ \ Var(X) = \frac{1-p}{p^2}\, $ |
| Uniform random variable over (a,b) | $ \,E[X] = \frac{a+b}{2},\ \ Var(X) = \frac{(b-a)^2}{12}\, $ |
| Gaussian random variable with parameter $ \mu \mbox{ and } \sigma^2 $ | $ \,E[X] = \mu,\ \ Var(X) = \sigma^2\, $ |
| Exponential random variable with parameter $ \lambda $ | $ \,E[X] = \frac{1}{\lambda},\ \ Var(X) = \frac{1}{\lambda^2}\, $ |
