(→Minimum Mean-Square Estimation (MMSE)) |
|||
| Line 35: | Line 35: | ||
Law of Iterated Expectation: E[E[X|Y]]=E[X] | Law of Iterated Expectation: E[E[X|Y]]=E[X] | ||
| + | ==Hypothesis Testing== | ||
| − | == | + | ===ML Rule=== |
Given a value of X, we will say H1 is true if X is in region R, else will will say H0 is true. | Given a value of X, we will say H1 is true if X is in region R, else will will say H0 is true. | ||
| Line 50: | Line 51: | ||
:<math>Pr(\mbox{Say }H_0|H_1) = Pr(x \in R^C|\theta_1)</math> | :<math>Pr(\mbox{Say }H_0|H_1) = Pr(x \in R^C|\theta_1)</math> | ||
| − | == | + | ===MAP Rule=== |
:<math>\mbox{Overall P(err)} = P_{\theta}(\theta_{0})Pr\Big[\mbox{Say }H_{1}|H_{0}\Big] +P_{\theta}(\theta_{1})Pr\Big[\mbox{Say }H_{0}|H_{1}\Big] </math> | :<math>\mbox{Overall P(err)} = P_{\theta}(\theta_{0})Pr\Big[\mbox{Say }H_{1}|H_{0}\Big] +P_{\theta}(\theta_{1})Pr\Big[\mbox{Say }H_{0}|H_{1}\Big] </math> | ||
| − | ==Likelihood Ratio Test== | + | ===Likelihood Ratio Test=== |
'''''How to find a good rule?''''' | '''''How to find a good rule?''''' | ||
Revision as of 15:35, 13 December 2008
Contents
Maximum Likelihood Estimation (ML)
- $ \hat a_{ML} = \overset{max}{a} f_{X}(x_i;a) $ continuous
- $ \hat a_{ML} = \overset{max}{a} Pr(x_i;a) $ discrete
Maximum A-Posteriori Estimation (MAP)
- $ \hat \theta_{MAP}(x) = \text{arg }\overset{max}{\theta} P_{X|\theta}(x|\theta)P_ {\theta}(\theta) $
- $ \hat \theta_{MAP}(x) = \text{arg }\overset{max}{\theta} f_{X|\theta}(x|\theta)P_ {\theta}(\theta) $
Minimum Mean-Square Estimation (MMSE)
- $ \hat{y}_{\rm MMSE}(x) = \int_{-\infty}^{\infty} {y}{f}_{\rm Y|X}(y|x)\, dy={E}[Y|X=x] $
Law Of Iterated Expectation
- $ E[E[X|Y]] = \begin{cases} \sum_{y} E[X|Y = y]p_Y(y),\,\,\,\,\,\,\,\,\,\,\mbox{ Y discrete,}\\ \int_{-\infty}^{+\infty} E[X|Y = y]f_Y(y)\,dy,\mbox{ Y continuous.} \end{cases} $
Using the total expectation theorem:
- $ E\Big[ E[X|Y]] = E[X] $
Mean Square Error
- $ MSE = E[(\theta - \hat \theta(x))^2] $
Linear Minimum Mean-Square Estimation (LMMSE)
- $ \hat{y}_{\rm LMMSE}(x) = E[\theta]+\frac{COV(x,\theta)}{Var(x)}(x-E[x]) $
Law of Iterated Expectation: E[E[X|Y]]=E[X]
Hypothesis Testing
ML Rule
Given a value of X, we will say H1 is true if X is in region R, else will will say H0 is true.
Type I error
Say $ H_1 $ when truth is $ H_0 $. Probability of this is:
- $ Pr(\mbox{Say } H_1|H_0) = Pr(x \in R|\theta_0) $
Type II error
Say $ H_0 $ when truth is $ H_1 $. Probability of this is:
- $ Pr(\mbox{Say }H_0|H_1) = Pr(x \in R^C|\theta_1) $
MAP Rule
- $ \mbox{Overall P(err)} = P_{\theta}(\theta_{0})Pr\Big[\mbox{Say }H_{1}|H_{0}\Big] +P_{\theta}(\theta_{1})Pr\Big[\mbox{Say }H_{0}|H_{1}\Big] $
Likelihood Ratio Test
How to find a good rule? --Khosla 16:44, 13 December 2008 (UTC)
$ \ L(x) = \frac{P_{\rm X|\theta} (x|\theta_1)}{P_{\rm X|\theta} (x|\theta_0)} $
Choose threshold (T),
- $ \mbox{Say } \begin{cases} H_{1}; \mbox{ if } L(x) > T\\ H_{0}; \mbox{ if } L(x) < T \end{cases} $
The Maximum Likelihood rule is a Likelihood Ratio Test with T = 1
Observations:
- as T increases Type I Error Increases
- as T increases Type II Error Decreases
- as T decreases Type I Error Decreases
- as T decreases Type II Error Increases
