Search results for "Conditional probability" - Rhea

Create the page "Conditional probability" on this wiki! See also the search results found.

• ECE302 First Bonus point opportunity yanwen jin
  ...y:ECE302Spring2013Boutin]] [[Category:ECE]] [[Category:ECE302]] [[Category:probability]] [[Category:problem solving]] [[Category:conditional probability]]
  1 KB (175 words) - 11:45, 28 January 2013
• ECE302 EC1 Woo
  ...Category:probability]] [[Category:problem solving]] [[Category:conditional probability]] =Conditional Probability Problem=
  1 KB (212 words) - 12:34, 27 January 2013
• Lecture4 blog ECE302S13 Boutin
  ...probabilities can be used to obtain the probability of false alarm and the probability of missed detection in a detection experiment. **[[Practice_Question_Monty_Hall_ECE302S13Boutin|Use Conditional Probability to explain the solution of the Monty Hall Problem]]
  2 KB (336 words) - 08:08, 25 January 2013
• Practice Question Monty Hall ECE302S13Boutin
  = [[:Category:Problem solving|Practice Problem on]] Conditional Probability = ...oncept of conditional probability to explain why switching door leads to a probability of success equal to 2/3.
  7 KB (1,241 words) - 13:49, 13 February 2013
• Bonus point 1 ECE302 Spring2012 Boutin
  Invent a problem related to conditional probability and/or independence and solve it. Then post your problem and solution on a [[Category:probability]]
  3 KB (489 words) - 10:10, 4 February 2013
• EC1-Boldt
  ...Category:probability]] [[Category:problem solving]] [[Category:conditional probability]] =Practice Problem on Probability ( [[ECE302]] )=
  2 KB (279 words) - 12:39, 26 January 2013
• ECE 302-Extra Credit- Zimmerman
  '''Conditional Probability''' One dice is rolled two separate times. Find the probability that the dice lands on an even number both times, and the sum of the two ro
  1 KB (143 words) - 19:18, 27 January 2013
• Green26 ece302 ec1
  ...Category:probability]] [[Category:problem solving]] [[Category:conditional probability]] [[Category:independence]] (a) Assuming that we have an equal probability of sampling a pixel from each image (ie <math style='inline'>P(im_1) = P(im
  5 KB (779 words) - 19:36, 27 January 2013
• BonusProblem Zhenming Zhang
  ...y:ECE302Spring2013Boutin]] [[Category:ECE]] [[Category:ECE302]] [[Category:probability]] [[Category:problem solving]] [[Category:conditional probability]]
  1 KB (181 words) - 11:47, 28 January 2013
• ECE301 conditional probability bonus
  ...[Category:probability]] [[Category:problem solving]][[Category:conditional probability]]
  770 B (129 words) - 08:10, 28 January 2013
• Bonus point 2 ECE302 Spring2012 Boutin
  ...int_1_ECE302_Spring2012_Boutin|invented a problem]] related to conditional probability and/or independence and solved it. We are inviting you to go over [[Bonus_p = Link to pages with student-created problems on conditional probability and/or independence =
  2 KB (361 words) - 11:13, 28 January 2013
• Lecture17 blog ECE302S13 Boutin
  ...degree". We subsequently finished illustrating the concept of conditional probability for discrete random variables. We then covered the concept of independent d
  2 KB (321 words) - 11:12, 15 February 2013
• Bayesian Decision Theory
  ...suming that the problems are posed in probabilistic terms and all relevant probability values are known (It is important to note that in reality its not always li ...bility ''P(x₁)'' that the next card is diamonds, and some prior probability ''P(x₂)'' that it is spades, and both probabilities sum up to 1
  5 KB (844 words) - 23:32, 28 February 2013
• Bayesian Decision Theory - Continuous Features
  ...states exactly how costly each chosen action is, and is used to convert a probability determination into a decision. Cost functions enables us to look at situati ..._j'', therefore by using Bayes formula we can find the posterior probability ''P''(''x_j''|'''Y'''):
  5 KB (893 words) - 16:27, 1 March 2013
• Lecture24 blog ECE302S13 Boutin
  ...ariables. We finished the lecture by giving the definition of conditional probability density function and illustrating it with an example. ::[[Practice_Question_probability_meeting_occurs_ECE302S13Boutin|Compute the probability that a meeting will occur]]
  2 KB (324 words) - 13:11, 5 March 2013
• Practice Question find conditional pdf ECE302S13Boutin
  [[Category:conditional density function]] = [[:Category:Problem_solving|Practice Problem]]: What is the conditional density function=
  1 KB (157 words) - 11:59, 26 March 2013
• Practice Question find conditional ellipse ECE302S13Boutin
  [[Category:conditional density function]] = [[:Category:Problem_solving|Practice Problem]]: What is the conditional density function=
  1,022 B (148 words) - 12:00, 26 March 2013
• Practice Question find conditional event ECE302S13Boutin
  [[Category:conditional density function]] = [[:Category:Problem_solving|Practice Problem]]: What is the conditional density function=
  2 KB (299 words) - 09:17, 27 March 2013
• Lecture25 blog ECE302S13 Boutin
  ...niform distribution on a circle of radius r. We also saw the definition of conditional density when the condition is an event B (instead of the event "random vari ...ice_Question_find_conditional_ellipse_ECE302S13Boutin|Find the conditional probability density function (again)]]
  3 KB (350 words) - 11:24, 6 March 2013
• HW6 ECE302 S13 Boutin
  *[[Practice_Question_probability_meeting_occurs_ECE302S13Boutin|Compute the probability that a meeting will occur]] ...ractice_Question_find_conditional_pdf_ECE302S13Boutin|Find the conditional probability density function]]
  2 KB (340 words) - 03:37, 27 March 2013
• Bayes theorem S13
  [[Category:conditional probability]] <pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>
  4 KB (649 words) - 13:08, 25 November 2013
• Temporary file ECE302S12Boutin
  [[Category:probability]] What is the probability that the meeting will occur?
  3 KB (559 words) - 07:02, 22 March 2013
• Illustration of The Bayesian Decision Theory
  ...on Theory, showing how conditional probabilities are used to determine the probability of a particular event given that we know the prior probabilities. For this ...an or not. Without the information about the length of the last names, the probability of a student being African would always be 0.4, but with the added feature,
  3 KB (415 words) - 18:34, 22 March 2013
• Hw6 discussion sln ECE302 S13 Boutin
  *[[Practice_Question_probability_meeting_occurs_ECE302S13Boutin|Compute the probability that a meeting will occur]] ...ractice_Question_find_conditional_pdf_ECE302S13Boutin|Find the conditional probability density function]]
  2 KB (333 words) - 18:02, 2 April 2013
• Discriminant Functions For The Normal(Gaussian) Density
  ...nbsp;&nbsp;&nbsp;&nbsp;Discriminant functions are used to find the minimum probability of error in decision making problems. In a problem with feature vector '''y ...ub>'' being the state of nature, and ''P''(''w_j'') is the prior probability that nature is in state ''w_j''. If we take p('''Y'''|''w<sub>i</
  5 KB (844 words) - 05:43, 13 April 2013
• ECE600 F13 notes mhossain
  [[Category:probability]] *[[ECE600_F13_probability_spaces_mhossain|Probability Spaces]]
  2 KB (227 words) - 12:10, 21 May 2014
• ECE600 F13 probability spaces mhossain
  [[ECE600_F13_Conditional_probability_mhossain|Next Topic: Conditional Probability]] [[Category:probability]]
  20 KB (3,448 words) - 12:11, 21 May 2014
• ECE600 F13 Conditional probability mhossain
  [[ECE600_F13_probability_spaces_mhossain|Previous Topic: Probability Spaces]]<br/> [[Category:probability]]
  6 KB (1,023 words) - 12:11, 21 May 2014
• ECE600 F13 Statistical Independence mhossain
  [[ECE600_F13_Conditional_probability_mhossain|Previous Topic: Conditional Probability]]<br/> [[Category:probability]]
  9 KB (1,543 words) - 12:11, 21 May 2014
• ECE600 F13 rv distribution mhossain
  [[ECE600_F13_rv_conditional_distribution_mhossain|Next Topic: Conditional Distributions]] [[Category:probability]]
  15 KB (2,637 words) - 12:11, 21 May 2014
• ECE600 F13 rv conditional distribution mhossain
  [[Category:probability]] <font size= 3> Topic 7: Random Variables: Conditional Distributions</font size>
  6 KB (1,109 words) - 12:11, 21 May 2014
• ECE600 F13 rv Functions of random variable mhossain
  [[ECE600_F13_rv_conditional_distribution_mhossain|Previous Topic: Conditional Distributions]]<br/> [[Category:probability]]
  9 KB (1,723 words) - 12:11, 21 May 2014
• ECE600 F13 Expectation mhossain
  [[Category:probability]] ==Conditional Expectation==
  8 KB (1,474 words) - 12:12, 21 May 2014
• ECE600 F13 Joint Distributions mhossain
  [[Category:probability]] ...math>_Y</math> or pmf p<math>_Y</math> when Y = g(X), expectation E[g(X)], conditional expectation E[g(X)|M], and characteristic function <math>\Phi_X</math>. We
  8 KB (1,524 words) - 12:12, 21 May 2014
• ECE600 F13 Conditional Distributions for Two Random Variables mhossain
  [[Category:probability]] <font size= 3> Topic 15: Conditional Distributions for Two Random Variables</font size>
  6 KB (1,139 words) - 12:12, 21 May 2014
• ECE600 F13 Conditional Expectations for Two Random Variables mhossain
  [[Category:probability]] <font size= 3> Topic 16: Conditional Expectation for Two Random Variables</font size>
  4 KB (875 words) - 12:13, 21 May 2014
• From Bayes Theorem to Pattern Recognition via Bayes Rule
  [[Category:probability]] * The axioms of probability
  14 KB (2,241 words) - 10:42, 22 January 2015
• Upper Bounds for Bayes Error
  ...it is used to classify continuously valued data. Then 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, and therefore becomes a very impor
  13 KB (2,062 words) - 10:45, 22 January 2015
• Bayes Rule for 1-dimensional and N-dimensional feature spaces
  ...ath>P(B)</math>. By the definition of the conditional probability, a joint probability of <math>A</math> and <math>B</math>, <math>P(A, B)</math>, is the product ...hese are what we already know. With these information, we can say that the probability that the person who he had a conversation with was a woman is
  19 KB (3,255 words) - 10:47, 22 January 2015
• Bayes Rule Minimize Risk Dennis Lee
  In class we discussed Bayes rule for minimizing the probability of error. Our goal is to generalize this rule to minimize risk instead of probability of error.
  12 KB (1,810 words) - 10:46, 22 January 2015
• Bayersian Parameter Estimation
  ...information provided by the training data to help determine both the class-conditional densities and the priori probabilities. ...th>x_1, x_2, ... , x_n</math> drawn independently according to the unknown probability density <math>p(x)</math>.
  8 KB (1,268 words) - 08:31, 29 April 2014
• Bayes rule in practice review
  ...side should be divided by Prob(x) according to the property of conditional probability. ii) The vertical axis of Fig. 2 is just labeled “histogram.” I would s
  2 KB (259 words) - 12:40, 2 May 2014
• Estimation Using Nearest Neighbor
  ...variable which means <math>\theta' = w_{i}</math> is same as a posteriori probability <math>P(w_{i}|x').</math> If sample sizes are big enough, it could be assum ...lity of error as <math>P(e|x)</math>. Using this the unconditional average probability of error which indicates the average error according to training samples ca
  14 KB (2,313 words) - 10:55, 22 January 2015
• Bayersian Parameter Estimation: Gaussian Case
  ...on the observation on above equations, it can be concluded that both class-conditional densities and the priori could be obtained based on the training data. ...<math>\theta</math> to be a vector (random variable). More specifically, a probability function given a class condition of D and a parameter vector of <math>\thet
  10 KB (1,625 words) - 10:51, 22 January 2015
• Parzen Window Density Estimation
  ...tions defined in the normal way, which is correct. As one might guess, the probability distributions that are used to map samples to classes are not always of imm ...ut a substantial amount of information about the distribution of data (and conditional distributions of data belonging to each class) it is near impossible to do
  16 KB (2,703 words) - 10:54, 22 January 2015
• CBR logistic regression
  ...of the data. Instead, it takes the data as given and tries to maximize the conditional density (Prob(class|data)) directly. ...the probability. We want to say that given a hair length is 10 inches, the probability of the person being a female is close to 1.
  9 KB (1,540 words) - 10:56, 22 January 2015
• Derivation of Bayes rule Anonymous7
  ...that illustrates Bayes rule and how it can be used to update or revise the probability. Bayes Rule is an important rule in probability theory that allows to update or revise our theories when new evidence is gi
  7 KB (1,106 words) - 10:42, 22 January 2015
• Bayes Parameter Estimation with examples
  First recall that the joint probability density function of <math>(\mathbf X,\theta)</math> is the mapping on <math Next recall that the (marginal) probability density function f of <math>X</math> is given by
  10 KB (1,600 words) - 10:52, 22 January 2015
• NearestNeighbor
  ...variable which means <math>\theta' = w_{i}</math> is same as a posteriori probability <math>P(w_{i}|x').</math> If sample sizes are big enough, it could be assum ...lity of error as <math>P(e|x)</math>. Using this the unconditional average probability of error which indicates the average error according to training samples ca
  14 KB (2,323 words) - 04:54, 1 May 2014
• NNM
  ...variable which means <math>\theta' = w_{i}</math> is same as a posteriori probability <math>P(w_{i}|x').</math> If sample sizes are big enough, it could be assum ...lity of error as <math>P(e|x)</math>. Using this the unconditional average probability of error which indicates the average error according to training samples ca
  14 KB (2,340 words) - 17:24, 12 May 2014