Search results for "Binomial distribution" - Rhea

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• Questions ECE302Fall2008sanghavi
  ...binomial distribution and make n large and p small, do you get the poisson distribution ?)
  326 B (57 words) - 08:07, 12 September 2008
• Homework4 ECE302Fall2008sanghavi
  == Problem 1: Binomial Proofs == Let <math>X</math> denote a binomial random variable with parameters <math>(N, p)</math>.
  6 KB (883 words) - 12:55, 22 November 2011
• 4.3 Tiffany Sukwanto ECE302Fall2008sanghavi
  ...out the PMF and from there you can determine the type of distribution (eg. Binomial, Geometric, etc.)
  440 B (83 words) - 09:04, 23 September 2008
• 4.3a Priyanka Savkar ECE302Fall2008sanghavi
  This part deals with Binomial Random Variables. In this case, p = 1/5. Plug that into the PMF binomial variable formula, where the parameters are (n-k, l).
  401 B (68 words) - 15:04, 23 September 2008
• 4.3 Monsu Mathew ECE302Fall2008sanghavi
  By definition W is a binomial random variable so it's distribution (PMF) can be represented by:
  278 B (50 words) - 15:27, 23 September 2008
• 4.3 Carlos Leon ECE302Fall2008sanghavi
  PMF = (n over k) * p^k * (1-p)^k ==> pmf function for a binomial R.V. ...ion we can pretty much neglect the questions he already know (think of the distribution of this questions as a constant that won't affect the randomness of the out
  911 B (166 words) - 01:31, 24 September 2008
• Problem 1 - Tiffany Sukwanto ECE302Fall2008sanghavi
  If the number of photons captured followed binomial distribution of n=1000000 and p, then we can definitely apply the ML estimate formula. H
  235 B (38 words) - 19:32, 10 November 2008
• ECE662:Homework 1 Old Kiwi
  ...se maximum likelihood estimation to estimate the parameters of the feature distribution. Experiment to illustrate the accuracy of the classifier obtained with this ...ic principles, illustrating the centeral limit theorem when the underlying distribution is normal or chi-square or uniform, bowtie, right wedge, left wedge, and t
  10 KB (1,594 words) - 11:41, 24 March 2008
• Lecture 7 - MLE and BPE Old Kiwi
  ...MLE Examples: Binomial and Poisson Distributions_Old Kiwi|Examples of MLE: Binomial and Poisson Distributions]] [[MLE Examples: Binomial and Poisson Distributions_Old Kiwi|Examples of MLE: Binomial and Poisson Distributions]]
  10 KB (1,488 words) - 10:16, 20 May 2013
• Conjugate priors Old Kiwi
  family for L if the resulting posterior distribution p(y|x) is also in P for any likelihood function p(x|y). ...likelihood function happened to be a Binomial, you will have a posterior distribution which is also in the Beta family. This is quite useful because in this fram
  931 B (161 words) - 08:46, 10 April 2008
• MLE Examples: Exponential and Geometric Distributions Old Kiwi
  [[Category:exponential distribution]] [[Category:geometric distribution]]
  3 KB (498 words) - 10:13, 20 May 2013
• MLE Examples: Binomial and Poisson Distributions Old Kiwi
  [[Category:binomial distribution]] [[Category:poisson distribution]]
  2 KB (366 words) - 10:14, 20 May 2013
• ECE600 Example Addition of two independent Poisson random variables
  .../math> is binomially distributed, and determine the parameters of binomial distribution (<math>n</math> and <math>p</math>). .../math> is binomially distributed, and determine the parameters of binomial distribution (<math>n</math> and <math>p</math>).
  3 KB (557 words) - 12:11, 25 September 2013
• ECE 600 Prerequisites Discrete Random Variables
  ='''1.4.1 Bernoulli distribution'''= ='''1.4.2 Binomial distribution'''=
  5 KB (921 words) - 11:25, 30 November 2010
• ECE 600 Finals MRB 2004 Final
  This is the characteristic function of Binomial with probability pr . ...> because we already know that <math class="inline">\mathbf{M}</math> is Binomial random variable with probability pr .
  12 KB (2,205 words) - 07:20, 1 December 2010
• ECE600 QE 2005 August
  Assume that <math class="inline">\mathbf{X}</math> is a binomial distributed random variable with probability mass function (pmf) given by < ...bf{X}_{1},\mathbf{X}_{2},\cdots,\mathbf{X}_{n},\cdots</math> converges in distribution to a Poisson random variable having mean <math class="inline">\lambda</math
  10 KB (1,754 words) - 08:30, 27 June 2012
• ECE 600 Exams Addition of two independent Poisson random variables
  ...ath> is binomially distributed, and determine the parameters of binomial distribution (<math class="inline">n</math> and <math class="inline">p</math> ). This is a binomial pmf <math class="inline">b(n,p)</math> with parameters <math class="inline
  3 KB (532 words) - 11:58, 30 November 2010
• Probability Distribution
  | <math> F- </math> distribution | Binomial <math> B(n,p) </math>
  6 KB (851 words) - 15:34, 23 April 2013
• Lecture 7 - MLE and BPE OldKiwi
  ...MLE Examples: Binomial and Poisson Distributions_Old Kiwi|Examples of MLE: Binomial and Poisson Distributions]] [[MLE Examples: Binomial and Poisson Distributions_OldKiwi|[MLE Examples: Binomial and Poisson Distributions]]
  10 KB (1,472 words) - 11:16, 10 June 2013
• Lecture13ECE662S12
  ...we talked about Maximum Likelihood Estimation (MLE) of the parameters of a distribution. *[[MLE_Examples:_Binomial_and_Poisson_Distributions_OldKiwi|MLE example: binomial and poisson distributions]]
  2 KB (196 words) - 09:54, 23 April 2012