(Hypothesis Testing: ML Rule)
(Linear Minimum Mean-Square Estimation (LMMSE))
Line 30: Line 30:
 
==Linear Minimum Mean-Square Estimation (LMMSE)==
 
==Linear Minimum Mean-Square Estimation (LMMSE)==
  
<math>\hat{y}_{\rm LMMSE}(x) = E[\theta]+\frac{COV(x,\theta)}{Var(x)}\times (x-E[x])</math>
+
:<math>\hat{y}_{\rm LMMSE}(x) = E[\theta]+\frac{COV(x,\theta)}{Var(x)}(x-E[x])</math>
  
 
Law of Iterated Expectation: E[E[X|Y]]=E[X]
 
Law of Iterated Expectation: E[E[X|Y]]=E[X]

Revision as of 16:02, 13 December 2008

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)}(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) $

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:

  1. as T increases Type I Error Increases
  2. as T increases Type II Error Decreases
  3. as T decreases Type I Error Decreases
  4. as T decreases Type II Error Increases

Alumni Liaison

Questions/answers with a recent ECE grad

Ryne Rayburn