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| + | [[Category:ECE302Fall2008_ProfSanghavi]] | ||
| + | [[Category:probabilities]] | ||
| + | [[Category:ECE302]] | ||
| + | [[Category:cheat sheet]] | ||
| + | |||
| + | =[[ECE302]] Cheet Sheet number 1= | ||
You can get/put ideas for what should be on the cheat sheet here. <b> DO NOT SIGN YOUR NAME </b> | You can get/put ideas for what should be on the cheat sheet here. <b> DO NOT SIGN YOUR NAME </b> | ||
'''Sample Space, Axioms of probability (finite spaces, infinite spaces)''' | '''Sample Space, Axioms of probability (finite spaces, infinite spaces)''' | ||
| − | <math> P(A) \geq 0 </math> for all events A | + | 1. <math> P(A) \geq 0 </math> for all events A |
| + | |||
| + | 2. <math>P(\omega)=1</math> | ||
| + | |||
| + | 3. If A & B are disjoint then <math>P(A\cup B)=P(A)+P(B)</math> | ||
| + | |||
'''Properties of Probability laws''' | '''Properties of Probability laws''' | ||
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<math>P(A|B) = \frac{P(A \cap B)}{P(B)}</math> | <math>P(A|B) = \frac{P(A \cap B)}{P(B)}</math> | ||
| − | + | Properties: | |
1) <math>P(A|B) \ge 0</math> | 1) <math>P(A|B) \ge 0</math> | ||
| − | 2) <math>P( \Omega |B) | + | 2) <math>P( \Omega |B) = 1\!</math> |
| − | 3) if A1 and A2 are disjoint | + | 3) if A1 and A2 are disjoint <math>P(A1 \cup A2|B) = P(A1|B) + P(A2|B)</math> |
| − | + | ||
| − | <math>P(A1 \cup A2|B) = P(A1|B) + P(A2|B)</math> | + | |
'''Bayes rule and total probability''' | '''Bayes rule and total probability''' | ||
| − | <math>P( | + | <math>P(B)=P(B\cap A_1) + P(B \cap\ A_2) +...+P(B\cap A_n)= P(B|A_1)P(A)+P(B|A_2)P(A_2)+...+P(B|A_n)P(A_n)</math> |
'''Definitions of Independence and Conditional independence''' | '''Definitions of Independence and Conditional independence''' | ||
| + | Independence: | ||
| + | A & B are independent if <math>P(A\cap B)=P(A)P(B)</math> | ||
| + | side note: if A&B are independent then P(A|B)=P(A) | ||
| + | |||
| + | Conditional Independence: | ||
| + | A&B are conditionally independent given C if <math>P(A\cap B|C)=P(A|C)P(B|C)=\frac{P(A\cap C)}{P(C)} \frac{P(B\cap C)}{P(C)}</math> | ||
'''Definition and basic concepts of random variables, PMFs''' | '''Definition and basic concepts of random variables, PMFs''' | ||
| + | Random Variable: a map/function from outcomes to real values | ||
| − | + | Probability Mass Function (PMF) | |
| − | + | <math>P_X (x) = P(X=x)</math> | |
| − | + | '''The common random variables:''' bernoulli, binomial, geometric, and how they come about in problems. Also their PMFs. | |
| − | + | Geometric RV: | |
| − | + | where X is # of trials until the first success | |
| + | <math>P(X=k) = p(1-p)^{(k-1)}</math> for k>=1 | ||
| + | |||
| + | <math> E[X] = 1/p \!</math> | ||
| + | |||
| + | <math>Var(x)=\frac{(1-p)}{p^2}</math> | ||
| + | |||
| + | |||
| + | Binomial R.V. "many biased coins" | ||
| + | with parameters n and p where n is the number of outcomes. | ||
| + | |||
| + | where X is # of successes in n trials and is the sum of independent, identically distributed outcomes. | ||
| + | |||
| + | P(X=k) = nCk * p^k * (1-p)^(n-k) for k=0,1,2,...n | ||
| + | |||
| + | E[X]=np VAR[X]=np(1-p) | ||
| + | |||
| + | Bernoulli R.V "one biased coin" | ||
| + | with parameter p | ||
| + | |||
| + | X=1 if A occurs and X=0 otherwise | ||
| + | |||
| + | P(1)=p | ||
| + | |||
| + | E[x]=p | ||
| + | |||
| + | Var(X)=p(1-p) | ||
'''Definition of expectation and variance''' and their properties | '''Definition of expectation and variance''' and their properties | ||
| − | <math> Var(X) = E[X^2] - (E[X])^2 </math> | + | |
| + | <math>E[X]=\sum_X x P_X (x)</math> | ||
| + | |||
| + | <math>E[ax+b]=aE[x]+b</math> where a and b are constants | ||
| + | |||
| + | <math> Var(X) = E[X^2] - (E[X])^2 \!</math> | ||
| + | |||
| + | <math>Var(ax+b)=a^2 Var(x) </math> | ||
'''Joint PMFs of more than one random variable''' | '''Joint PMFs of more than one random variable''' | ||
| + | |||
| + | Joint Probability Mass Function | ||
| + | |||
| + | <math>P_{XY}(x,y)=P({X=x}\cap {Y=y})</math> | ||
| + | |||
| + | PX(x)=(SUM of all y)[PXY(x,y)] | ||
| + | |||
| + | PY(y)=(SUM of all x)[PXY(x,y)] | ||
| + | |||
| + | <math>E[g(X,Y)]=\sum_{X,Y} g(X,Y)P_{XY}(x,y)</math> | ||
| + | |||
| + | E[ax+by]=aE[x]+bE[y] | ||
| + | ---- | ||
| + | [[Main_Page_ECE302Fall2008sanghavi|Back to ECE302 Fall 2008 Prof. Sanghavi]] | ||
Latest revision as of 13:04, 22 November 2011
ECE302 Cheet Sheet number 1
You can get/put ideas for what should be on the cheat sheet here. DO NOT SIGN YOUR NAME
Sample Space, Axioms of probability (finite spaces, infinite spaces)
1. $ P(A) \geq 0 $ for all events A
2. $ P(\omega)=1 $
3. If A & B are disjoint then $ P(A\cup B)=P(A)+P(B) $
Properties of Probability laws
Definition of conditional probability, and properties thereof
$ P(A|B) = \frac{P(A \cap B)}{P(B)} $
Properties:
1) $ P(A|B) \ge 0 $
2) $ P( \Omega |B) = 1\! $
3) if A1 and A2 are disjoint $ P(A1 \cup A2|B) = P(A1|B) + P(A2|B) $
Bayes rule and total probability
$ P(B)=P(B\cap A_1) + P(B \cap\ A_2) +...+P(B\cap A_n)= P(B|A_1)P(A)+P(B|A_2)P(A_2)+...+P(B|A_n)P(A_n) $
Definitions of Independence and Conditional independence
Independence: A & B are independent if $ P(A\cap B)=P(A)P(B) $ side note: if A&B are independent then P(A|B)=P(A)
Conditional Independence: A&B are conditionally independent given C if $ P(A\cap B|C)=P(A|C)P(B|C)=\frac{P(A\cap C)}{P(C)} \frac{P(B\cap C)}{P(C)} $
Definition and basic concepts of random variables, PMFs
Random Variable: a map/function from outcomes to real values
Probability Mass Function (PMF) $ P_X (x) = P(X=x) $
The common random variables: bernoulli, binomial, geometric, and how they come about in problems. Also their PMFs.
Geometric RV:
where X is # of trials until the first success
$ P(X=k) = p(1-p)^{(k-1)} $ for k>=1
$ E[X] = 1/p \! $
$ Var(x)=\frac{(1-p)}{p^2} $
Binomial R.V. "many biased coins"
with parameters n and p where n is the number of outcomes.
where X is # of successes in n trials and is the sum of independent, identically distributed outcomes.
P(X=k) = nCk * p^k * (1-p)^(n-k) for k=0,1,2,...n
E[X]=np VAR[X]=np(1-p)
Bernoulli R.V "one biased coin" with parameter p
X=1 if A occurs and X=0 otherwise
P(1)=p
E[x]=p
Var(X)=p(1-p)
Definition of expectation and variance and their properties
$ E[X]=\sum_X x P_X (x) $
$ E[ax+b]=aE[x]+b $ where a and b are constants
$ Var(X) = E[X^2] - (E[X])^2 \! $
$ Var(ax+b)=a^2 Var(x) $
Joint PMFs of more than one random variable
Joint Probability Mass Function
$ P_{XY}(x,y)=P({X=x}\cap {Y=y}) $
PX(x)=(SUM of all y)[PXY(x,y)]
PY(y)=(SUM of all x)[PXY(x,y)]
$ E[g(X,Y)]=\sum_{X,Y} g(X,Y)P_{XY}(x,y) $
E[ax+by]=aE[x]+bE[y]
