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Confidence level of <math> (1-a) </math> if <math>Pr[\hat \theta \text{-} \delta < \theta < \hat \theta + \delta] >= (1-a) | Confidence level of <math> (1-a) </math> if <math>Pr[\hat \theta \text{-} \delta < \theta < \hat \theta + \delta] >= (1-a) | ||
for all \theta</math> | for all \theta</math> | ||
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| + | ==Definition of the term Unbiased estimators== | ||
| + | |||
| + | The ML estimator is said to be UNBIASED if it s expected value is the true value for all positive true values. | ||
| + | |||
| + | ==Few equations from previous material== | ||
| + | |||
| + | ::X and Y are independent | ||
| + | |||
| + | *<math>E[X] = \int^\infty_{-\infty}x*f_X(x)dx\!</math> | ||
| + | |||
| + | *<math>E[XY] = E[X]E[Y]\!</math> | ||
| + | |||
| + | *<math>Var(X) = E[X^2] - (E[X])^2\!</math> | ||
| + | |||
| + | Marginal PDF | ||
| + | |||
| + | *<math>E(g(x))=\int^\infty_{-\infty} g(x)f_X(x,y) dy</math> | ||
| + | |||
| + | *<math>f_X(x) = \int^\infty_{-\infty} f_{XY}(x,y) dy</math> | ||
| + | |||
| + | *<math>f_Y(y) = \int^\infty_{-\infty} f_{XY}(x,y) dx</math> | ||
Revision as of 18:33, 19 November 2008
Contents
- 1 Covariance
- 2 Correlation Coefficient
- 3 Markov Inequality
- 4 Chebyshev Inequality
- 5 Weak Law of Large Numbers
- 6 ML Estimation Rule
- 7 MAP Estimation Rule
- 8 Bias of an Estimator, and Unbiased estimators
- 9 Confidence Intervals, and how to get them via Chebyshev
- 10 Definition of the term Unbiased estimators
- 11 Few equations from previous material
Covariance
- $ COV(X,Y)=E[(X-E[X])(Y-E[Y])]\! $
- $ COV(X,Y)=E[XY]-E[X]E[Y]\! $
X and Y are uncorrelated if cov(X,Y) = 0
Correlation Coefficient
$ \rho(X,Y)= \frac {cov(X,Y)}{\sqrt{var(X)} \sqrt{var(Y)}} \, $
-1 <= $ \rho(X,Y) <= 1 $
Markov Inequality
Loosely speaking: In a nonnegative RV has a small mean, then the probability that it takes a large value must also be small.
- $ P(X \geq a) \leq E[X]/a\! $
for all a > 0
EXAMPLE:
On average it takes 1 hour to catch a fish. What is (an upper bound) the probability it will take 3 hours?
SOLUTION:
Using Markov's inequality, where $ E[X]\! $ = 1 and a = 3.
$ P(X \geq 3) \leq \frac {E[X]}{3} = \frac{1}{3} $
so 1/3 is the upper bound to the probability that it will take more than 3 hours to catch a fish.
Chebyshev Inequality
"Any RV is likely to be close to its mean"
- $ \Pr(\left|X-E[X]\right|\geq C)\leq\frac{var(X)}{C^2}. $
Weak Law of Large Numbers
The weak law of large numbers states that the sample average converges in probability towards the expected value
- $ \overline{X}_n \, \xrightarrow{P} \, \mu \qquad\textrm{for}\qquad n \to \infty. $
Mn = (X1 + ... + Xn)/n = X1/n + ... + Xn/n
E[Mn] = nE[X]/n = E[X]
Var[Mn] = Var(X1/n) + ... + Var(Xn/n) = Var(X)/n
Pr[ |Mn - E[X]| >= Var(Mn)/$ \sigma^2 = Var(X)/n\sigma^2 $
ML Estimation Rule
$ \hat a_{ML} = \text{max}_a ( f_{X}(x_i;a)) $ continuous
$ \hat a_{ML} = \text{max}_a ( Pr(x_i;a)) $ discrete
If X is a binomial (n,p), where is X is number of heads n tosses,
Then, for any fixed k-value;
$ \hat p_{ML}(k) = k/n $
If X is exponential then it's ML estimate is:
$ \frac{1}{ \overline{X}} $
MAP Estimation Rule
$ \hat \theta_{MAP} = \text{argmax}_\theta ( f_{\theta|X}(\theta|x)) $
Which can be expanded and turned into the following (if I am not mistaken):
$ \hat \theta_{MAP} = \text{argmax}_\theta ( f_{X|\theta}(x|\theta)f_{\theta}(\theta)) $
for discrete case:
$ \hat \theta_{MAP} = \text{argmax}_\theta ( P_{X|\theta}(x|\theta)P_{\theta}(\theta)) $
Bias of an Estimator, and Unbiased estimators
An estimator is unbiased if: $ E[\hat a_{ML}] = a $ for all values of a
Confidence Intervals, and how to get them via Chebyshev
$ \theta \text{ is unknown and fixed} $
$ \hat \theta \text{ is random and should be close to } \theta \text{ most of the time} $
$ if Pr[|\hat \theta \text{-} \theta|] <= (1-a) \text { then we say we have (1-a) confidence in the interval } [\hat \theta - E, \hat \theta + E] $
Confidence level of $ (1-a) $ if $ Pr[\hat \theta \text{-} \delta < \theta < \hat \theta + \delta] >= (1-a) for all \theta $
Definition of the term Unbiased estimators
The ML estimator is said to be UNBIASED if it s expected value is the true value for all positive true values.
Few equations from previous material
- X and Y are independent
- $ E[X] = \int^\infty_{-\infty}x*f_X(x)dx\! $
- $ E[XY] = E[X]E[Y]\! $
- $ Var(X) = E[X^2] - (E[X])^2\! $
Marginal PDF
- $ E(g(x))=\int^\infty_{-\infty} g(x)f_X(x,y) dy $
- $ f_X(x) = \int^\infty_{-\infty} f_{XY}(x,y) dy $
- $ f_Y(y) = \int^\infty_{-\infty} f_{XY}(x,y) dx $
