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- 21:28, 14 December 2008 (diff | hist) . . (+140) . . Cheat4 ECE302Fall2008sanghavi (→Maximum A-Posteriori Estimation (MAP))
- 15:54, 8 December 2008 (diff | hist) . . (+148) . . N Anand Gautam 10.4 ECE302Fall2008sanghavi (New page: :<math>\operatorname{MSE}(\hat{\theta})=\operatorname{Var}\left(\hat{\theta}\right)+ \left(\operatorname{Bias}(\hat{\theta},\theta)\right)^2.</math>)
- 15:54, 8 December 2008 (diff | hist) . . (+46) . . Homework10 ECE302Fall2008sanghavi (→Problem 4: Digital Loss)
- 10:56, 3 December 2008 (diff | hist) . . (+488) . . N Binary Hypothesis Testing (12/3) ECE302Fall2008sanghavi (New page: Decision Rule: A map from values of x to Ho or H1 if x E R, say H1 else if x does not contain R say Ho Max-likelihood Rule: Pick hypothesis that maxes conditional PDF ML Rule: say H1 i...)
- 10:56, 3 December 2008 (diff | hist) . . (-1) . . Main Page ECE302Fall2008sanghavi (→Helpful Examples)
- 10:56, 3 December 2008 (diff | hist) . . (+489) . . N Hypothesis Testing Example (12/3) ECE302Fall2008sanghavi (New page: Decision Rule: A map from values of x to Ho or H1 if x E R, say H1 else if x does not contain R say Ho Max-likelihood Rule: Pick hypothesis that maxes conditional PDF ML Rule: say H1 ...) (current)
- 10:43, 3 December 2008 (diff | hist) . . (-11) . . Main Page ECE302Fall2008sanghavi (→Helpful Examples)
- 10:41, 3 December 2008 (diff | hist) . . (+352) . . N Decision Theory and Hypothesis Testing (12/3) ECE302Fall2008sanghavi (New page: Dice Ho: fair Px|theta(X|theta 0) = 1/8 for all x Dice H1: loaded Px|theta(X|theta1) = 1/4 if x =1,2 = 1/8 if x =3,4,5,6 my rule = R = {1, 2, 3} Prob...) (current)
- 10:33, 3 December 2008 (diff | hist) . . (+75) . . Main Page ECE302Fall2008sanghavi (→Helpful Examples)
- 07:11, 19 November 2008 (diff | hist) . . (+119) . . Cheat3 ECE302Fall2008sanghavi (→MAP Estimation Rule)
- 19:15, 10 November 2008 (diff | hist) . . (+80) . . Problem 1 - Anand Gautam ECE302Fall2008sanghavi (current)
- 19:14, 10 November 2008 (diff | hist) . . (+726) . . N Problem 1 - Anand Gautam ECE302Fall2008sanghavi (New page: how to differentiate Now suppose we had only one coin but its ''p'' could have been any value 0 ≤ ''p'' ≤ 1. We must maximize the likelihood function: :<math> L(\theta) = f_D(\mat...)
- 19:13, 10 November 2008 (diff | hist) . . (+53) . . Homework9 ECE302Fall2008sanghavi (→Problem 1: Imperfect camera)
- 18:52, 10 November 2008 (diff | hist) . . (+16) . . Problem 3 - Patrick M. Avery Jr. ECE302Fall2008sanghavi (current)
- 17:04, 10 November 2008 (diff | hist) . . (+13) . . Problem 1 - Beau Morrison ECE302Fall2008sanghavi (current)
- 17:00, 10 November 2008 (diff | hist) . . (+83) . . Problem 1 - Beau Morrison ECE302Fall2008sanghavi
- 16:21, 10 November 2008 (diff | hist) . . (+142) . . N Problem 2 - Anand Gautam ECE302Fall2008sanghavi (New page: What kind of RV is used to determine the Probability of Max Likelihood> And how does the number of pulses that radar transmits come into play?) (current)
- 16:08, 10 November 2008 (diff | hist) . . (+55) . . Homework9 ECE302Fall2008sanghavi (→Problem 2: Imperfect Radar)
- 19:55, 3 November 2008 (diff | hist) . . (0) . . Homework8 ECE302Fall2008sanghavi (→Problem 2: Bounded Variance)
- 19:40, 3 November 2008 (diff | hist) . . (+127) . . N Anand Gautam -2.c ECE302Fall2008sanghavi (New page: use the following two equations and solve for the unknown: <math> E[X^2] = E[X] = p \!</math> <math> Var(X) = p(1-p)\!</math>) (current)
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