(→Likelihood Ratio TEST) |
(→Likelihood Ratio TEST) |
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<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 | + | ==Likelihood Ratio Test== |
'''''How to find a good rule?''''' | '''''How to find a good rule?''''' | ||
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Choose threshold (T), | Choose threshold (T), | ||
− | <math>\mbox{Say } | + | :<math>\mbox{Say } |
\begin{cases} | \begin{cases} | ||
H_{1}; \mbox{ if } L(x) > T\\ | H_{1}; \mbox{ if } L(x) > T\\ | ||
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\end{cases}</math> | \end{cases}</math> | ||
− | + | The Maximum Likelihood rule is a Likelihood Ratio Test with T = 1 | |
'''Observations''': | '''Observations''': | ||
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#as T decreases Type I Error Decreases | #as T decreases Type I Error Decreases | ||
#as T decreases Type II Error Increases | #as T decreases Type II Error Increases | ||
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Revision as of 14:59, 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\limits_{-\infty}^{\infty}\ {y}{f}_{\rm Y|X}(y|x)\, dy={E}(Y|X=x) $
Law Of Iterated Expectation
Unconditional Expectaion--$ \ E[X] = E[E[x|\theta]] $
--Umang 16:10, 13 December 2008 (UTC)umang
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)}\times (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 H1 when truth is H0. Probability of this is: $ Pr(\mbox{Say } H_1|H_0) = Pr(x \in R|\theta_0) $
Type II error
Say H0 when truth is H1. Probability of this is: $ Pr(\mbox{Say }H_0|H_1) = Pr(x \in R^C|\theta_1) $
Hypothesis Testing: 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_1)} $
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