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| align="right" style="padding-right: 1em;" | Gaussian random variable with parameter <math>\mu \mbox{ and } \sigma^2</math>
 
| align="right" style="padding-right: 1em;" | Gaussian random variable with parameter <math>\mu \mbox{ and } \sigma^2</math>
 
| <math>\,E[X] = \mu,\ \ Var(X) = \sigma^2\,</math>  
 
| <math>\,E[X] = \mu,\ \ Var(X) = \sigma^2\,</math>  
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|-
 +
| align="right" style="padding-right: 1em;" | Exponential random variable with parameter <math>\lambda</math>
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| <math>\,E[X] = \frac{1}{\lambda},\ \ Var(X) = \frac{1}{\lambda^2}\,</math>
 
|}
 
|}
  

Revision as of 11:34, 22 October 2010

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}\, $

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Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang