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Bayes Decision Rule Old Kiwi

=Bayes Decision Rule Video= The video demonstrates Bayes decision rule on 2D feature data from two classes. We visualize the decision hyper surfac

1 KB (172 words) - 11:08, 10 June 2013
From Bayes Theorem to Pattern Recognition via Bayes Rule

[[Category:Bayes' Rule]] '''From Bayes' Theorem to Pattern Recognition via Bayes' Rule''' 

14 KB (2,241 words) - 10:42, 22 January 2015
Bayes Rule for 1-dimensional and N-dimensional feature spaces

Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces ...nal feature space. So, we will take a look at what the definition of Bayes rule is, how it can be used for the classification task with examples, and how w

19 KB (3,255 words) - 10:47, 22 January 2015
Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014

Bayes Rule to Minimize Risk ==Part 1: Introduction - Revisit Bayes Rule/Classifier ==

2 KB (226 words) - 10:45, 22 January 2015
Bayes Rule Minimize Risk Dennis Lee

'''Bayes Rule for Minimizing Risk''' In class we discussed Bayes rule for minimizing the probability of error.

12 KB (1,810 words) - 10:46, 22 January 2015
Comments Bayes Rule Dennis Lee

Comments for [[Bayes_Rule_Minimize_Risk_Dennis_Lee| Bayes Rule for Minimizing Risk]] ...t function which is the Expected Risk, and finally states a classification rule that would minimize it. Then, he gives two clear examples for 1D and 2D fea

3 KB (504 words) - 16:04, 30 April 2014
Derivation of Bayes Rule from Bayes Theorem

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' 

628 B (83 words) - 18:52, 20 April 2014
Derivation of Bayes' Rule from Bayes' Theorem

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' 

927 B (122 words) - 10:42, 22 January 2015
Bayes rule in practice

'''Bayes rule in practice''' A [http://www.projectrhea.org/le ...ng data with unknown parameters, and testing data is classified with Bayes rule. 

7 KB (1,177 words) - 10:47, 22 January 2015
Slecture Bayes rule to minimize risk Andy Park review

...[[Slecture_Bayes_rule_to_minimize_risk_Andy_Park_ECE662_Spring_2014| Bayes Rule to Minimize Risk]]''' ...erical deviration. Finally, likelihood ratio test is associated with Bayes rule.

2 KB (303 words) - 09:59, 12 May 2014
Bayes rule in practice review

Questions and Comments for: '''[[Bayes rule in practice|Bayes rule in practice]]''' ...the estimated parameters were used to classify the testing data with Bayes rule.

2 KB (259 words) - 12:40, 2 May 2014
Derivation Bayes Rule slecture ECE662 Spring2014 Kim

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule ''' 

924 B (123 words) - 10:43, 22 January 2015
Derivation of Bayes rule In Greek

[[Category:Bayes' Rule]] '''Derivation of Bayes rule (In Greek)''' 

18 KB (665 words) - 10:43, 22 January 2015
Bayes rule

=Bayes rule=

1 KB (171 words) - 06:18, 29 April 2014
Derivation of Bayes rule In Chinese

Bayes Rule and Its Applications 

6 KB (535 words) - 10:43, 22 January 2015
Derivation of Bayes rule Anonymous7

Derivation of Bayes Rule * Bayes rule statement.

7 KB (1,106 words) - 10:42, 22 January 2015
Slecture optimality bayes decision rule michaux ECE662S14

== Proof of the Optimality of Bayes' Decision Rule ==

774 B (101 words) - 10:43, 22 January 2015
Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces Review

..._1-dimensional_and_N-dimensional_feature_spaces|Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces]] ...at use Bayes theorem. The author then discussed classification using Byes rule and derived the error formula for calculating the error when classifying 1

2 KB (359 words) - 09:58, 3 May 2014
Derivation of Bayes rule In Chinese review

...excellent. It definitely gives a good and fairly complete review of Bayes' rule. It is also very well organized, first the definition (What is Bayes' thero LZ Comment1: I like your example for Bayes' rule. It is a simple, typical and real-world problem of solving the posterior pr

1 KB (223 words) - 19:55, 3 May 2014
Introduction to Bayes' Rule

[[Category:Bayes Rule]] '''Introduction to Bayes' Rule in Layman's Terms''' 

892 B (116 words) - 10:42, 22 January 2015

Page text matches

HW2.5 Landis Huffman ECE302Fall2008sanghavi

Hint: Recall Bayes' Rule:

111 B (26 words) - 06:40, 4 September 2008
HW3.4c AJ Hartnett ECE302Fall2008sanghavi

Following Bayes rule we can get:

620 B (135 words) - 06:56, 16 September 2008
Cheat1 ECE302Fall2008sanghavi

'''Bayes rule and total probability'''

3 KB (525 words) - 13:04, 22 November 2011
Cheat2 ECE302Fall2008sanghavi

== Continuous Bayes' rule: ==

4 KB (722 words) - 13:05, 22 November 2011
Map Estimation ECE302Fall2008sanghavi

Using Bayes' Rule, we can expand the posterior <math>f_{\theta | X}(\theta | X)</math>:

4 KB (671 words) - 09:23, 10 May 2013
ECE662:BoutinSpring08 Old Kiwi

* [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi]] * [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi]]

6 KB (747 words) - 05:18, 5 April 2013
ECE662:Glossary Old Kiwi

== [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] == Bayes' decision rule creates an objective function which minimizes the probability of error (mis

31 KB (4,832 words) - 18:13, 22 October 2010
ECE662:Homework 1 Old Kiwi

...er program that classifies the feature vectors according to Bayes decision rule. Generate some artificial (normally distributed) data, and test your progra

10 KB (1,594 words) - 11:41, 24 March 2008
Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (938 words) - 08:38, 17 January 2013
Lecture 1 - Introduction Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

3 KB (468 words) - 08:45, 17 January 2013
Lecture 2 - Decision Hypersurfaces Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (737 words) - 08:45, 17 January 2013
Lecture 3 - Bayes classification Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (843 words) - 08:46, 17 January 2013
Lecture 5 - Discriminant Functions Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (916 words) - 08:47, 17 January 2013
Lecture 6 - Discriminant Functions Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

9 KB (1,586 words) - 08:47, 17 January 2013
Lecture 7 - MLE and BPE Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

10 KB (1,488 words) - 10:16, 20 May 2013
Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (792 words) - 08:48, 17 January 2013
Lecture 9 - Linear Discriminant Functions Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,307 words) - 08:48, 17 January 2013
Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (755 words) - 08:48, 17 January 2013
Lecture 11 - Fischer's Linear Discriminant again Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (907 words) - 08:49, 17 January 2013
Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,235 words) - 08:49, 17 January 2013
Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,354 words) - 08:51, 17 January 2013
Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

13 KB (2,073 words) - 08:39, 17 January 2013
Lecture 15 - Parzen Window Method Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

7 KB (1,212 words) - 08:38, 17 January 2013
Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

10 KB (1,607 words) - 08:38, 17 January 2013
Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (1,066 words) - 08:40, 17 January 2013
ECE662:ChangeLog Old Kiwi

...he section on [[Lecture 3 - Bayes classification_Old Kiwi#Bayes_rule|Bayes rule]] equation <3,4,5> and figures <1,2,3>. ...Clustering Methods_Old Kiwi]] by adding the section on how the separation rule obtained by mixture of Gaussians model can be generalized to future unseen

10 KB (1,418 words) - 12:21, 28 April 2008
Bayesian Decision Theory Old Kiwi

...PROBABILITY and LIKELIHOOD by forming a POSTERIOR probability using Bayes Rule.

3 KB (558 words) - 17:03, 16 April 2008
ROC curves Old Kiwi

...ass 1 is more likely than class 2, and we select class 1. Applying Bayes' rule, and canceling the p(x):

3 KB (621 words) - 08:48, 10 April 2008
Publications Old Kiwi

...any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it ...al supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropaga

39 KB (5,715 words) - 10:52, 25 April 2008
Lecture 4 - Bayes Classification Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,360 words) - 08:46, 17 January 2013
Bayes Decision Rule Old Kiwi

=Bayes Decision Rule Video= The video demonstrates Bayes decision rule on 2D feature data from two classes. We visualize the decision hyper surfac

1 KB (172 words) - 11:08, 10 June 2013
Lecture 19 - Nearest Neighbor Error Rates Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

5 KB (1,003 words) - 08:40, 17 January 2013
Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (1,047 words) - 08:42, 17 January 2013
Lecture 21 - Decision Trees(Continued) Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (1,012 words) - 08:42, 17 January 2013
Lecture 22 - Decision Trees and Clustering Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

6 KB (806 words) - 08:42, 17 January 2013
Bayes Example Old Kiwi

...PROBABILITY and LIKELIHOOD by forming a POSTERIOR probability using Bayes Rule.

2 KB (302 words) - 01:09, 7 April 2008
Editing technique Old Kiwi

...each region, we can observe some samples which are misclassified by Bayes rule. Removing these misclassfied sample will generate two homogeneous sets of s The followings are the algorithm of the editing technique for the K-NN rule:

2 KB (296 words) - 11:48, 7 April 2008
Lecture 23 - Spanning Trees Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

7 KB (1,060 words) - 08:43, 17 January 2013
Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,254 words) - 08:43, 17 January 2013
Lecture 25 - Clustering Algorithms Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,259 words) - 08:43, 17 January 2013
Bayes classification Old Kiwi

== Bayes rule == Bayes rule addresses the predefined classes classification problem.

2 KB (399 words) - 14:03, 18 June 2008
Lecture 26 - Statistical Clustering Methods Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,244 words) - 08:44, 17 January 2013
Lecture 27 - Clustering by finding valleys of densities Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

8 KB (1,337 words) - 08:44, 17 January 2013
Lecture 28 - Final lecture Old Kiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],

10 KB (1,728 words) - 08:55, 17 January 2013
Lecture 2 - Decision Hypersurfaces OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

5 KB (744 words) - 11:17, 10 June 2013
ECE662:BoutinSpring08 OldKiwi

...tion Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]] ...nd Metrics(Continued)_OldKiwi|Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)]]

7 KB (875 words) - 07:11, 13 February 2012
ECE662

*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]

3 KB (429 words) - 09:07, 11 January 2016
Test

== '''2.1 Classifier using Bayes rule''' == ...{i} \mid x \big) </math>. So instead of solving eq.(2.1), we use the Bayes rule to change the problem to

17 KB (2,590 words) - 10:45, 22 January 2015
Lecture 9 - Linear Discriminant Functions OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

9 KB (1,341 words) - 11:15, 10 June 2013
2010 Spring ECE 662 mboutin

*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] *[[Hw1 ECE662Spring2010|HW1- Bayes rule for normally distributed features]]

4 KB (547 words) - 12:24, 25 June 2010
ECE662 topic2 discussions

=Is Bayes' Rule Truly the Best?=

535 B (72 words) - 10:09, 1 March 2010
EE662Sp10BayesExample

Or, equivalently, we can use Bayes' Rule explicity. Bayes' Rule is:

7 KB (948 words) - 04:35, 2 February 2010
Hw1 ECE662Spring2010

:Experiment with Bayes rule for normally distributed features. Summarize your experiments, results, and

1 KB (149 words) - 09:07, 6 October 2010
ECE662 hw1 discussions

...10|here]]) is a freeform exercise that consists in applying Bayes decision rule to Normally distributed data. The next homework will consists in a peer rev

4 KB (596 words) - 13:17, 12 November 2010
Star feedbackECE662S2010

...Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|page on Bayes rule under severe class imbalance]] ...esome [[EE662Sp10OptimalPrediction|page discussing the optimality of Bayes rule]].

7 KB (1,009 words) - 11:27, 13 April 2010
OutlineECE662S10

| 4. Bayes Rule *The nearest neighbor classification rule.

1 KB (165 words) - 08:55, 22 April 2010
Hw2 ECE662Spring2010

Experiment with making decisions using Bayes rule and parametric density estimation. Summarize your experiments, results, and

849 B (115 words) - 15:33, 10 May 2010
Hw3 ECE662Spring2010

Experiment with making decisions using Bayes rule and non-parametric density estimation. Summarize your experiments, results,

904 B (122 words) - 15:16, 10 May 2010
Lecture4ECE662S10

...ntroduced [[Bayes_Decision_Theory|Bayes rule]] for making decisions. (This rule is the basis for this course.) We focused our discussion on the case where

649 B (85 words) - 11:41, 13 April 2010
Lecture5ECE662S10

...that the example previously proposed performs worse]] than following Bayes rule. ...expected loss (called "risk") when following [[Bayes_Decision_Theory|Bayes rule]].

968 B (131 words) - 11:42, 13 April 2010
Lecture6ECE662S10

...minant functions]] and their relationship to [[Bayes_Decision_Theory|Bayes rule]]. We focused on discriminant functions when the class densities are normal

462 B (56 words) - 08:48, 11 May 2010
Bayes Rate Fallacy: Bayes Rules under Severe Class Imbalance

[[Category:Bayes decision rule]] [[Bayes Decision Theory|Bayes decision rule]] is a simple, intuitive and powerful classifier. It allows to select the m

5 KB (694 words) - 12:41, 2 February 2012
Lecture23ECE662S10

...ut [[Bayes Rate Fallacy: Bayes Rules under Severe Class Imbalance‎|Bayes rule under severe class imbalance]]. Please join in!

1 KB (210 words) - 09:20, 15 April 2010
Bayes Decision Theory

...uld choose the most likely class given the observation. By following Bayes rule, one achieves the minimum possible probability of error. ...ture_3_-_Bayes_classification_OldKiwi|Lecture 3 introducing Bayes decision rule]]

2 KB (222 words) - 09:25, 15 April 2010
FavoritedecisionECE662S10

...osing the class with the higher prior. [[EE662Sp10OptimalPrediction|Bayes rule is optimal]]. - jvaught

6 KB (884 words) - 16:26, 9 May 2010
Lecture8ECE662S10

...orem. We then discussed the probability of error when using Bayes decision rule. More precisely, we obtained the Chernoff Bound and the Bhattacharrya bound

628 B (86 words) - 09:09, 11 May 2010
Noteslecture8ECE662S10

Error bounds for Bayes decision rule: As we know Bayes decision rule guarantees the lowest average error rate; It Does not tell what the probabi

5 KB (806 words) - 09:08, 11 May 2010
Presentation Use of MATLAB in classroom

*[[Homework_1_OldKiwi|Experimenting with Bayes rule]] (from [[ECE662]])

2 KB (286 words) - 05:45, 29 December 2010
Decision Theory Glossary ECE662Spring2008

== [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] == Bayes' decision rule creates an objective function which minimizes the probability of error (mis

31 KB (4,787 words) - 18:21, 22 October 2010
SA1050

*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]

1 KB (164 words) - 06:47, 18 November 2010
Machine learning tutorials

*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]

1 KB (156 words) - 12:26, 27 March 2015
Machine learning slectures

...imality_bayes_decision_rule_michaux_ECE662S14|Optimality of Bayes Decision Rule]], by Aaron Michaux

1 KB (140 words) - 12:14, 27 March 2015
ECE 600 Prerequisites Bayes' Theorem

• = Bayes' rule

717 B (138 words) - 11:23, 30 November 2010
Lecture4ECE662S12

...decision theory today, namely Bayes decision rule. We first presented the rule for discrete-valued feature vectors, and illustrated it using the previousl ...student's notes for Lecture 3 from ECE662 Spring 2008]] (introducing Bayes Rule)

2 KB (259 words) - 12:30, 23 February 2012
Lecture5ECE662S12

...covered the discriminant functions that could be used to implement such a rule.

1 KB (187 words) - 12:30, 23 February 2012
Lecture6ECE662S12

Today we began talking about an important subject in decision theory: Bayes rule for normally distributed feature vectors. We proposed a simple discriminant

2 KB (298 words) - 12:31, 23 February 2012
Hw1 ECE662 S12

[[Category:Bayes rule]] Experiment with Bayes rule for normally distributed features. Summarize your experiments, results, and

918 B (134 words) - 13:18, 8 March 2012
Hw1 discussion ECE662 S12

[[Category:Bayes rule]]

2 KB (320 words) - 12:21, 12 February 2012
Lecture 1 - Introduction OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

3 KB (413 words) - 11:17, 10 June 2013
Lecture 3 - Bayes classification OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (874 words) - 11:17, 10 June 2013
Lecture 4 - Bayes Classification OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,403 words) - 11:17, 10 June 2013
Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

10 KB (1,609 words) - 11:22, 10 June 2013
Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (977 words) - 11:22, 10 June 2013
Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

7 KB (1,098 words) - 11:22, 10 June 2013
Lecture 6 - Discriminant Functions OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

10 KB (1,604 words) - 11:17, 10 June 2013
Lecture 7 - MLE and BPE OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

10 KB (1,472 words) - 11:16, 10 June 2013
Lecture 5 - Discriminant Functions OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (946 words) - 11:17, 10 June 2013
Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (833 words) - 11:16, 10 June 2013
Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (813 words) - 11:18, 10 June 2013
Lecture 11 - Fischer's Linear Discriminant again OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

6 KB (946 words) - 11:18, 10 June 2013
Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,278 words) - 11:19, 10 June 2013
Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

9 KB (1,389 words) - 11:19, 10 June 2013
Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

13 KB (2,098 words) - 11:21, 10 June 2013
Lecture 15 - Parzen Window Method OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,246 words) - 11:21, 10 June 2013
Lecture 19 - Nearest Neighbor Error Rates OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

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Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

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Lecture 21 - Decision Trees(Continued) OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

7 KB (1,055 words) - 11:23, 10 June 2013
Lecture 22 - Decision Trees and Clustering OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

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Lecture 23 - Spanning Trees OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

7 KB (1,091 words) - 11:23, 10 June 2013
Lecture 24 - Clustering and Hierarchical Clustering OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

9 KB (1,276 words) - 11:24, 10 June 2013
Lecture 25 - Clustering Algorithms OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,299 words) - 11:24, 10 June 2013
Hw2 ECE662 S12

[[Category:Bayes rule]]

1 KB (164 words) - 14:25, 30 May 2012
Lecture 26 - Statistical Clustering Methods OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,214 words) - 11:24, 10 June 2013
Lecture 27 - Clustering by finding valleys of densities OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

8 KB (1,313 words) - 11:24, 10 June 2013
Lecture 28 - Final lecture OldKiwi

[[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|

10 KB (1,704 words) - 11:25, 10 June 2013
ECE-QE CS1-2011 solusion-1

\color{green}\text{It should be added: According to the Bayes rule:}

8 KB (1,247 words) - 10:29, 13 September 2013
Lecture6 blog ECE302S13 Boutin

In Lecture 6, we presented the total probability theorem and Bayes rule. We illustrated both of these using a chess tournament example. We also ill

3 KB (363 words) - 06:30, 23 January 2013
Green26 ece302 ec1

<math style='inline'>= \frac{P(im_3)P(R|im_3)}{P(R)}</math> from Bayes' rule

5 KB (779 words) - 19:36, 27 January 2013
Bayesian Decision Theory

...e we only have two variables). We can therefore use the following decision rule; that if ''P(x1)'' > ''P(x2)'', then the card is diam

5 KB (844 words) - 23:32, 28 February 2013
How to get started math squad Spring2013

**Probability: Computing the probability of false alarm using Bayes rule. Give examples related to diseases testing, pregnancy tests, radar detectio

3 KB (555 words) - 17:17, 18 March 2013
Bayes theorem S13

[[Category:bayes rule]] <pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>

4 KB (649 words) - 13:08, 25 November 2013
Bayes theorem eg1 S13

<pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>

4 KB (592 words) - 13:09, 25 November 2013
Bayes theorem eg2 S13

<pre>keyword: probability, false positive, Bayes' Theorem, Bayes' Rule </pre>

3 KB (562 words) - 13:09, 25 November 2013
Bayes theorem eg3 S13

<pre>keyword: probability, Monty Hall, Bayes' Theorem, Bayes' Rule </pre>

5 KB (925 words) - 13:09, 25 November 2013
Illustration of The Bayesian Decision Theory

== Illustration of Bayes Rule == ...ay, we will be looking at a real world illustration where we can use Bayes Rule to solve a problem.

3 KB (415 words) - 18:34, 22 March 2013
ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective

...tion Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]] ...nd Metrics(Continued)_OldKiwi|Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)]]

3 KB (425 words) - 09:59, 4 November 2013
2014 Spring ECE 662 Boutin

*Slectures on Bayes Rule **Bayes Rule in Layman's Terms

10 KB (1,450 words) - 20:50, 2 May 2016
From Bayes Theorem to Pattern Recognition via Bayes Rule

[[Category:Bayes' Rule]] '''From Bayes' Theorem to Pattern Recognition via Bayes' Rule''' 

14 KB (2,241 words) - 10:42, 22 January 2015
Lecture 3 - Bayes Theorem and its Application to Pattern Recognition OldKiwi

#REDIRECT [[From Bayes Theorem to Pattern Recognition via Bayes Rule]]

70 B (10 words) - 07:10, 12 February 2014
Upper Bounds for Bayes Error

...hen we will present the probability of error that results from using Bayes rule. When Bayes rule is used the resulting probability of error is the smallest possible error,

13 KB (2,062 words) - 10:45, 22 January 2015
Upper Bounds for Bayes Error Questions and comment

...ayes' Error” is concise and has good visuals. Dilshan starts with Bayes' rule for the continuous case and arrives at the formula for computing the probab

2 KB (257 words) - 08:51, 11 May 2014
Bayes Rule for 1-dimensional and N-dimensional feature spaces

Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces ...nal feature space. So, we will take a look at what the definition of Bayes rule is, how it can be used for the classification task with examples, and how w

19 KB (3,255 words) - 10:47, 22 January 2015
PCA

*A working example using PCA with Bayes rule in classification #Use PCA with Bayes rule in classification

22 KB (3,459 words) - 10:40, 22 January 2015
NeymanPearson中文Spring2014

'''Neyman-Pearson: How Bayes Decision Rule Controls Error''' A [https://www.projectrhea.or

7 KB (509 words) - 19:30, 2 May 2014
Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014

Bayes Rule to Minimize Risk ==Part 1: Introduction - Revisit Bayes Rule/Classifier ==

2 KB (226 words) - 10:45, 22 January 2015
Bayes Rule Minimize Risk Dennis Lee

'''Bayes Rule for Minimizing Risk''' In class we discussed Bayes rule for minimizing the probability of error.

12 KB (1,810 words) - 10:46, 22 January 2015
Comments Bayes Rule Dennis Lee

Comments for [[Bayes_Rule_Minimize_Risk_Dennis_Lee| Bayes Rule for Minimizing Risk]] ...t function which is the Expected Risk, and finally states a classification rule that would minimize it. Then, he gives two clear examples for 1D and 2D fea

3 KB (504 words) - 16:04, 30 April 2014
Derivation of Bayes Rule from Bayes Theorem

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' 

628 B (83 words) - 18:52, 20 April 2014
Derivation of Bayes' Rule from Bayes' Theorem

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' 

927 B (122 words) - 10:42, 22 January 2015
Bayes rule in practice

'''Bayes rule in practice''' A [http://www.projectrhea.org/le ...ng data with unknown parameters, and testing data is classified with Bayes rule. 

7 KB (1,177 words) - 10:47, 22 January 2015
Slecture Bayes rule to minimize risk Andy Park review

...[[Slecture_Bayes_rule_to_minimize_risk_Andy_Park_ECE662_Spring_2014| Bayes Rule to Minimize Risk]]''' ...erical deviration. Finally, likelihood ratio test is associated with Bayes rule.

2 KB (303 words) - 09:59, 12 May 2014
Bayes rule in practice review

Questions and Comments for: '''[[Bayes rule in practice|Bayes rule in practice]]''' ...the estimated parameters were used to classify the testing data with Bayes rule.

2 KB (259 words) - 12:40, 2 May 2014
KnnDensityEstimation

On applying Bayes Rule, the above equation becomes,

10 KB (1,743 words) - 10:54, 22 January 2015
Discussion about Discriminant Functions for the Multivariate Normal Density

[[Category:Bayes Rule]]

14 KB (2,287 words) - 10:46, 22 January 2015
Derivation Bayes Rule slecture ECE662 Spring2014 Kim

[[Category:Bayes Rule]] '''Derivation of Bayes' Rule ''' 

924 B (123 words) - 10:43, 22 January 2015
Jieun kim slecture derivation Bayes ruleECE662Spring2014 review

...[Derivation_Bayes_Rule_slecture_ECE662_Spring2014_Kim|Derivation of Bayes' Rule]]''' ...[Derivation_Bayes_Rule_slecture_ECE662_Spring2014_Kim|Derivation of Bayes' Rule]]'''

2 KB (290 words) - 17:58, 2 May 2014
Estimation Using Nearest Neighbor

...ocrustes metric could be a good example to understand the nearest neighbor rule.---- ...ation if sample sizes are guaranteed. In other words, the nearest neighbor rule is matching perfectly with probabilities in nature.

14 KB (2,313 words) - 10:55, 22 January 2015
Derivation of Bayes rule In Greek

[[Category:Bayes' Rule]] '''Derivation of Bayes rule (In Greek)''' 

18 KB (665 words) - 10:43, 22 January 2015
ParzenWindow

...h>Prob(w_{i0}|x_0) \geq Prob(w_i|x_0) \forall i=1,...,c</math> from Bayes' rule. In other words,

11 KB (1,824 words) - 10:53, 22 January 2015
662slecture tang

Bayes rule in practice: definition and parameter estimation *Bayes rule for Gaussian data

9 KB (1,382 words) - 10:47, 22 January 2015
Bayes rule

=Bayes rule=

1 KB (171 words) - 06:18, 29 April 2014
Parzen Window Density Estimation

Also recall that BPE involves using Bayes' rule to obtain the conditional distribution of the parameter vector <math>\theta

16 KB (2,703 words) - 10:54, 22 January 2015
ECE662 S14 Statistical Pattern recognition slectures collective

[[Category:Bayes' Rule]]

562 B (67 words) - 10:18, 29 April 2014
Derivation of Bayes rule In Chinese

Bayes Rule and Its Applications 

6 KB (535 words) - 10:43, 22 January 2015
ECE662 roc

And we use a binary decision rule <math>\ \phi(x)\ </math> as a function: ...R, the function <math>\ \phi(x)=1 \ </math> and we accept Ha. In Bayesian Rule, the priors probabilities of each hypothesis are assumed known, and approac

11 KB (1,823 words) - 10:48, 22 January 2015
CBR logistic regression

Having this curve, we could develop an decision rule:

9 KB (1,540 words) - 10:56, 22 January 2015
Derivation of Bayes rule Anonymous7

Derivation of Bayes Rule * Bayes rule statement.

7 KB (1,106 words) - 10:42, 22 January 2015
Slecture optimality bayes decision rule michaux ECE662S14

== Proof of the Optimality of Bayes' Decision Rule ==

774 B (101 words) - 10:43, 22 January 2015
Bayes Parameter Estimation with examples

if the parameter has a continuous distribution. Finally, according to Bayes rule, the conditional probability density function of <math>\theta</math> given Thus, the posterior, according to Bayes rule,

10 KB (1,600 words) - 10:52, 22 January 2015
Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces Review

..._1-dimensional_and_N-dimensional_feature_spaces|Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces]] ...at use Bayes theorem. The author then discussed classification using Byes rule and derived the error formula for calculating the error when classifying 1

2 KB (359 words) - 09:58, 3 May 2014
Knearestneighbors

...the optimal decision rule to classify a point <math> x_0 </math> is Bayes Rule, which is to choose the class for which <math> P(w_i|x_0) </math>, the is ...\hat{P}(x_0|w_c) </math> as above, and choose the class according to Bayes Rule: the class <math> c </math> such that <math> \hat{P}(x_0|w_c)\hat{P}(w_c) <

9 KB (1,604 words) - 10:54, 22 January 2015
Slecture review

Questions and Comments for: '''[[662slecture_tang| Bayes rule in practice: definition and parameter estimation]]''' Back to [[662slecture_tang| Bayes rule in practice: definition and parameter estimation]]

1 KB (241 words) - 14:01, 6 May 2014
MLEforGMM

  Following Bayes rule, the responsibility that a mixture component takes for explaining an observ ...es'_Theorem]] [[Category:Probability]] [[Category:Bayes'_Rule]] [[Category:Bayes'_Classifier]] [[Category:Slecture]] [[Category:ECE662Spring2014Boutin]] [[Ca

13 KB (1,966 words) - 10:50, 22 January 2015
NearestNeighbor

...ocrustes metric could be a good example to understand the nearest neighbor rule.---- ...ation if sample sizes are guaranteed. In other words, the nearest neighbor rule is matching perfectly with probabilities in nature.

14 KB (2,323 words) - 04:54, 1 May 2014
Derivation of Bayes rule In Chinese review

...excellent. It definitely gives a good and fairly complete review of Bayes' rule. It is also very well organized, first the definition (What is Bayes' thero LZ Comment1: I like your example for Bayes' rule. It is a simple, typical and real-world problem of solving the posterior pr

1 KB (223 words) - 19:55, 3 May 2014
NNM

...ocrustes metric could be a good example to understand the nearest neighbor rule.---- ...ation if sample sizes are guaranteed. In other words, the nearest neighbor rule is matching perfectly with probabilities in nature.

14 KB (2,340 words) - 17:24, 12 May 2014
ECE662Selecture zhenpengMLE

...o(x|w_i)</math>, <math>Prob(w_i)</math> <math>\rightarrow</math> use Bayes rule, or <math>\rho(x|w_i)</math> <math>\rightarrow</math> use Neyman-Pearson Cr

11 KB (2,046 words) - 10:51, 22 January 2015
Slecture derivation Bayes ruleECE662Spring2014 review

...'''[[Derivation of Bayes' Rule from Bayes' Theorem | Derivation of Bayes' Rule from Bayes' Theorem ...should be Evidence. I've never heard of estimation term used in the Bayes Rule, evidence is more common in my opinion. Overall, good job!

1 KB (221 words) - 20:58, 4 May 2014
Amichaux slecture derivation Bayes ruleECE662Spring2014 review

..._decision_rule_michaux_ECE662S14|Proof of the Optimality of Bayes Decision Rule]]''' ...rtitions of Ω and finally the proof that using Bayes' rule as a decisions rule yields an optimal result.

2 KB (351 words) - 04:32, 5 May 2014
Introduction to Bayes' Rule

[[Category:Bayes Rule]] '''Introduction to Bayes' Rule in Layman's Terms''' 

892 B (116 words) - 10:42, 22 January 2015
IntroToBayes Questions Comments

...nd Comments for: '''[[Introduction_to_Bayes%27_Rule|Introduction to Bayes' Rule in Layman's Terms]]''' Back to '''[[Introduction_to_Bayes%27_Rule|Introduction to Bayes' Rule in Layman's Terms]]'''

511 B (81 words) - 05:39, 5 May 2014
Ness slecture 2014

'''Neyman-Pearson: How Bayes Decision Rule Controls Error''' A [http://www.projectrhea.org

10 KB (793 words) - 10:46, 22 January 2015
Ness slecture QandA

Comments for [[ Ness slecture 2014|Neyman-Pearson: How Bayes Decision Rule Controls Error ]]

2 KB (283 words) - 16:37, 12 May 2014
2014 Spring ECE 662 Boutin Statistical Pattern recognition slectures

==2. Bayes Rule == *Bayes Rule in Layman's Terms

8 KB (1,123 words) - 10:38, 22 January 2015
Hw1 Bochum Su2014

c)Which problem among a) or b) above corresponds to Bayes decision rule? Why is that approach to making more decision more practical than the other

2 KB (245 words) - 01:57, 8 July 2014
Hw2 Bochum Su2014

...the ground truth-- the label of each data point). Then use Baye's decision rule (assuming perfect knowledge of the parameters of the class density) to clas

2 KB (248 words) - 03:16, 8 July 2014
Project1 peer review ECE662S16

...nt of the project was to investigate when the method (i.e., Bayes Decision Rule for normally distributed features) works and when it does not work.

3 KB (453 words) - 14:53, 19 February 2016
Project1 ECE662S16

[[Category:Bayes rule]] We have learned how to use Bayes Decision Rule to classify data points drawn at random from two classes that are normally

3 KB (434 words) - 17:19, 1 February 2016
Project2 ECE662S16

[[Category:Bayes rule]]

1 KB (238 words) - 13:32, 26 February 2016
Project3 ECE662S16

[[Category:Bayes rule]]

2 KB (302 words) - 19:11, 31 March 2016
Swanson MA279 Fall2022 topic4

The law of total probability is commonly used in Bayes' rule. It is very useful, because it provides a way to calculate probabilities by In many examples using Bayes' rule the probability P(B) in the denominator will be left and the law of total p

14 KB (2,441 words) - 16:10, 14 December 2022

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