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- Bayes' decision rule creates an objective function which minimizes the probability of error (misclassification). This method assumes a known distribution for ...an Parameter Estimation is a technique for parameter estimation which uses probability densities as estimates of the parameters instead of exact deterministic one31 KB (4,832 words) - 18:13, 22 October 2010
- * Parametric Estimation of Class Conditional Density The class conditional density <math>p(\vec{x}|w_i)</math> can be estimated using training data. W10 KB (1,488 words) - 10:16, 20 May 2013
- *Determining probability of a new point requires one calculation: P(x|theta) *Probabilistic (probability density) estimate of parameters, p(theta | Data)6 KB (995 words) - 10:39, 20 May 2013
- ...ty density function) and cdf (cumulative distribution function), or simply probability distribution function. The probability density function or pdf is defined as: <math>p({x}) = p(x_1,\cdots , x_n) =8 KB (1,360 words) - 08:46, 17 January 2013
- ...obability of one proposition given that another proposition holds. For the probability of proposition A given proposition B, we write P(A|B).</p> ...)</math> and <math>P(\lnot A \cap B)</math>. Therefore, we must divide the probability we are looking for, <math>P(A \cap B)</math>, by the sum of all probabiliti1 KB (245 words) - 12:18, 17 March 2008
- ...nd argument, holding the first fixed. Eg: consider a model which gives the probability density function of observable random variable X as a function of parameter708 B (126 words) - 01:55, 17 April 2008
- == Example of Turning Conditional Distributions Around == Suppose that the conditional distributions <math>P_{\mathbb{X}|\mathbb{Y}}</math> are empirically estima7 KB (948 words) - 04:35, 2 February 2010
- '''Topics Covered''': An introductory treatment of probability theory including distribution and density functions, moments and random var i. an ability to solve simple probability problems in electrical and computer engineering applications.2 KB (231 words) - 07:20, 4 May 2010
- Bayes' decision rule creates an objective function which minimizes the probability of error (misclassification). This method assumes a known distribution for ...an Parameter Estimation is a technique for parameter estimation which uses probability densities as estimates of the parameters instead of exact deterministic one31 KB (4,787 words) - 18:21, 22 October 2010
- [[Category:probability]] *[[Probability_Formulas|Probability Formulas]]2 KB (238 words) - 12:14, 25 September 2013
- [[Category:probability]] Question 1: Probability and Random Processes1 KB (191 words) - 17:42, 13 March 2015
- Find the conditional density of <math>\mathbf{Y}</math> conditioned on <math>\mathbf{X}=x</math> Find a maximum aposteriori probability estimator.7 KB (1,103 words) - 05:27, 15 November 2010
- What is the probability that this experiment terminates on or before the seventh coin toss? What is the probability that this experiment terminates with an even number of coin tosses?10 KB (1,827 words) - 08:33, 27 June 2012
- ...tion consists of two separate short questions relating to the structure of probability space: ...}P_{2}\left(A\right),\qquad\forall A\in\mathcal{F}</math> is also a valid probability measure on <math class="inline">\mathcal{F}</math> if <math class="inline"7 KB (1,210 words) - 08:31, 27 June 2012
- ...th> is the power set of <math class="inline">\mathcal{S}</math> , and the probability measure <math class="inline">\mathcal{P}</math> is specified by the pmf <m ...math class="inline">f_{\mathbf{X}}\left(x|\mathbf{Z}=z\right)</math> , the conditional pdf of <math class="inline">\mathbf{X}</math> given the event <math class=14 KB (2,358 words) - 08:31, 27 June 2012
- ..."inline">\mathbf{X}</math> is a binomial distributed random variable with probability mass function (pmf) given by <math class="inline">p_{n}\left(k\right)=\left ...random variables, with <math class="inline">\mathbf{X}_{n}</math> having probability mass function <math class="inline">p_{n}\left(k\right)=\left(\begin{array}{10 KB (1,754 words) - 08:30, 27 June 2012
- ...on values <math class="inline">0,1,2,\cdots</math> and having conditional probability mass function <math class="inline">p_{\mathbf{N}}\left(n|\left\{ \mathbf{X} Find the probability that \mathbf{N}=n .9 KB (1,560 words) - 08:30, 27 June 2012
- Find the conditional density of <math class="inline">\mathbf{Y}</math> conditioned on <math cla Find a maximum aposteriori probability estimator.2 KB (416 words) - 11:47, 3 December 2010
- [[Category:probability]] [https://www.projectrhea.org/learning/practice.php Practice Problems] on Probability7 KB (960 words) - 18:17, 23 February 2015
- ...ty density function) and cdf (cumulative distribution function), or simply probability distribution function. The probability density function or pdf is defined as: <math>p({x}) = p(x_1,\cdots , x_n) =8 KB (1,403 words) - 11:17, 10 June 2013
- * Parametric Estimation of Class Conditional Density The class conditional density <math>p(\vec{x}|w_i)</math> can be estimated using training data. W10 KB (1,472 words) - 11:16, 10 June 2013
- *Determining probability of a new point requires one calculation: P(x|theta) *Probabilistic (probability density) estimate of parameters, p(theta | Data)6 KB (976 words) - 13:25, 8 March 2012
- =Student Project for [[MA375]]: Mysteries of Probability= ...lity of an event, the more certain we are that the event will occur. Thus, probability in an applied sense is a measure of the confidence a person has that a (ran12 KB (2,113 words) - 06:50, 21 March 2013
- [[Category:probability]] Question 1: Probability and Random Processes5 KB (780 words) - 01:25, 9 March 2015
- [[Category:probability]] Question 1: Probability and Random Processes5 KB (735 words) - 01:17, 10 March 2015
- [[Category:probability]] Question 1: Probability and Random Processes4 KB (609 words) - 01:54, 10 March 2015
- [[Category:probability]] Question 1: Probability and Random Processes4 KB (572 words) - 10:24, 10 March 2015
- [[Category:probability]] Question 1: Probability and Random Processes5 KB (748 words) - 01:01, 10 March 2015
- [[Category:probability]] Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Edition,10 KB (1,422 words) - 20:14, 30 April 2013
- *1.2 Probability Models **Probability Laws (axioms, properties4 KB (498 words) - 10:18, 17 April 2013
- ...y:ECE302Spring2013Boutin]] [[Category:ECE]] [[Category:ECE302]] [[Category:probability]] [[Category:problem solving]] [[Category:conditional probability]]1 KB (175 words) - 11:45, 28 January 2013
- ...Category:probability]] [[Category:problem solving]] [[Category:conditional probability]] =Conditional Probability Problem=1 KB (212 words) - 12:34, 27 January 2013
- ...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
- = [[: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
- 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
- ...Category:probability]] [[Category:problem solving]] [[Category:conditional probability]] =Practice Problem on Probability ( [[ECE302]] )=2 KB (279 words) - 12:39, 26 January 2013
- '''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 ro1 KB (143 words) - 19:18, 27 January 2013
- ...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(im5 KB (779 words) - 19:36, 27 January 2013
- ...y:ECE302Spring2013Boutin]] [[Category:ECE]] [[Category:ECE302]] [[Category:probability]] [[Category:problem solving]] [[Category:conditional probability]]1 KB (181 words) - 11:47, 28 January 2013
- ...[Category:probability]] [[Category:problem solving]][[Category:conditional probability]]770 B (129 words) - 08:10, 28 January 2013
- ...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
- ...degree". We subsequently finished illustrating the concept of conditional probability for discrete random variables. We then covered the concept of independent d2 KB (321 words) - 11:12, 15 February 2013
- ...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(x<sub>1</sub>)'' that the next card is diamonds, and some prior probability ''P(x<sub>2</sub>)'' that it is spades, and both probabilities sum up to 15 KB (844 words) - 23:32, 28 February 2013
- ...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 ...<sub>j</sub>'', therefore by using Bayes formula we can find the posterior probability ''P''(''x<sub>j</sub>''|'''Y'''):5 KB (893 words) - 16:27, 1 March 2013
- ...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
- [[Category:conditional density function]] = [[:Category:Problem_solving|Practice Problem]]: What is the conditional density function=1 KB (157 words) - 11:59, 26 March 2013
- [[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
- [[Category:conditional density function]] = [[:Category:Problem_solving|Practice Problem]]: What is the conditional density function=2 KB (299 words) - 09:17, 27 March 2013
- ...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
- *[[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
