• [[Category:conditional probability]] <pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>
    4 KB (649 words) - 13:08, 25 November 2013
  • [[Category:probability]] What is the probability that the meeting will occur?
    3 KB (559 words) - 07:02, 22 March 2013
  • ...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
  • *[[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
  • ...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<sub>j</sub>'') is the prior probability that nature is in state ''w<sub>j</sub>''. If we take p('''Y'''|''w<sub>i</
    5 KB (844 words) - 05:43, 13 April 2013
  • [[Category:probability]] *[[ECE600_F13_probability_spaces_mhossain|Probability Spaces]]
    2 KB (227 words) - 12:10, 21 May 2014
  • [[ECE600_F13_Conditional_probability_mhossain|Next Topic: Conditional Probability]] [[Category:probability]]
    20 KB (3,448 words) - 12:11, 21 May 2014
  • [[ECE600_F13_probability_spaces_mhossain|Previous Topic: Probability Spaces]]<br/> [[Category:probability]]
    6 KB (1,023 words) - 12:11, 21 May 2014
  • [[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_conditional_distribution_mhossain|Next Topic: Conditional Distributions]] [[Category:probability]]
    15 KB (2,637 words) - 12:11, 21 May 2014
  • [[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_conditional_distribution_mhossain|Previous Topic: Conditional Distributions]]<br/> [[Category:probability]]
    9 KB (1,723 words) - 12:11, 21 May 2014
  • [[Category:probability]] ==Conditional Expectation==
    8 KB (1,474 words) - 12:12, 21 May 2014
  • [[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
  • [[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
  • [[Category:probability]] <font size= 3> Topic 16: Conditional Expectation for Two Random Variables</font size>
    4 KB (875 words) - 12:13, 21 May 2014
  • [[Category:probability]] * The axioms of probability
    14 KB (2,241 words) - 10:42, 22 January 2015
  • ...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
  • ...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
  • 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
  • ...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
  • ...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
  • ...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
  • ...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
  • ...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
  • ...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
  • ...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
  • 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
  • ...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
  • ...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
  • ...these parameters of the MEAN, the author goes on to derive the conditional probability of p(x|D) using the afore-mentioned parameters
    2 KB (300 words) - 17:04, 12 May 2014
  • [[Category:probability]] Question 1: Probability and Random Processes
    1 KB (187 words) - 01:03, 10 March 2015
  • [[Category:probability]] Question 1: Probability and Random Processes
    2 KB (366 words) - 01:36, 10 March 2015
  • [[Category:probability]] Question 1: Probability and Random Processes
    4 KB (679 words) - 01:58, 10 March 2015
  • [[Category:probability]] Question 1: Probability and Random Processes
    3 KB (454 words) - 10:25, 10 March 2015
  • [[Category:probability]] Question 1: Probability and Random Processes
    2 KB (351 words) - 00:17, 4 December 2015
  • [[Category:probability]] Question 1: Probability and Random Processes
    4 KB (851 words) - 23:04, 31 January 2016
  • [[Category:probability]] Question 1: Probability and Random Processes
    3 KB (502 words) - 15:33, 19 February 2019
  • ...“Stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event”. ...occurrence of S can be expressed as the multiplication of the conditional probability of occurrence of each word <math>w_{1}, w_{2}, ... w_{n}</math>. Therefore:
    8 KB (1,251 words) - 00:22, 6 December 2020
  • ...ter obtaining new data. The theorem describes the conditional probability (probability of one event occurring with some relationship to one or more other events)
    654 B (101 words) - 20:50, 6 December 2020
  • ...ter obtaining new data. The theorem describes the conditional probability (probability of one event occurring with some relationship to one or more other events)
    713 B (106 words) - 21:43, 6 December 2020
  • ...ter obtaining new data. The theorem describes the conditional probability (probability of one event occurring with some relationship to one or more other events) ...lates the probability of the hypothesis before getting the evidence to the probability of the hypothesis after getting the evidence, making P(A) the prior and P(B
    1 KB (212 words) - 22:21, 6 December 2020
  • Probability : Group 4 Probability
    14 KB (2,441 words) - 16:10, 14 December 2022

View (previous 50 | next 50) (20 | 50 | 100 | 250 | 500)

Alumni Liaison

has a message for current ECE438 students.

Sean Hu, ECE PhD 2009