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  • Homework9 ECE302Fall2008sanghavi
    ...re is an object. Assume <math>p_1 < p_2</math>. What is the max-likelihood estimation rule for whether the object is present or absent? == Problem 3: Exponential Parameter Estimation ==
    3 KB (500 words) - 12:50, 22 November 2011
  • Map Estimation ECE302Fall2008sanghavi
    =MAP Estimation by Landis= Given observation X used to estimate an unknown parameter <math>\theta</math> of distribution <math>f_x(X)</math>
    4 KB (671 words) - 09:23, 10 May 2013
  • Cheat3 ECE302Fall2008sanghavi
    ==ML Estimation Rule== ==MAP Estimation Rule==
    4 KB (820 words) - 13:06, 22 November 2011
  • ECE662:Glossary Old Kiwi
    == [[Bayesian Parameter Estimation_Old Kiwi|Bayesian Parameter Estimation]] == Bayesian Parameter Estimation is a technique for parameter estimation which uses probability densities as estimates of the parameters instead of
    31 KB (4,832 words) - 18:13, 22 October 2010
  • ECE662:Homework 1 Old Kiwi
    Take a subset of the data you used for Question 2. Use maximum likelihood estimation to estimate the parameters of the feature distribution. Experiment to illus ...ace the words “maximum likelihood estimation” by “Bayesian parameter estimation” in Question 3.
    10 KB (1,594 words) - 11:41, 24 March 2008
  • Lecture 7 - MLE and BPE Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]],
    10 KB (1,488 words) - 10:16, 20 May 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]],
    5 KB (792 words) - 08:48, 17 January 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]],
    8 KB (1,354 words) - 08:51, 17 January 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]],
    13 KB (2,073 words) - 08:39, 17 January 2013
  • Bayesian Parameter Estimation Old Kiwi
    ...PE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE662]], Spring 2010, Prof. Boutin == Estimation of mean, given a known covariance ==
    4 KB (707 words) - 10:37, 20 May 2013
  • Parametric Estimators Old Kiwi
    The MLE estimator is probably the most important parameter estimator in classical statistics. The reason is that the MLE estimator is Furthermore if <math>\hat \theta</math> is the MLE estimator of the parameter <math>\theta</math> , then <math>\sqrt{n}({\hat \theta}-\theta)</math> wil
    6 KB (995 words) - 10:39, 20 May 2013
  • Publications Old Kiwi
    ===A tutorial on Maximum Likelihood Estimation=== *'''In Jae Myung, "Tutorial on Maximum Estimation", Journal of Mathematical Psychology, vol. 47, pp. 90-100, 2003'''
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Comparison of MLE and Bayesian Parameter Estimation Old Kiwi
    =Comparison of MLE and Bayesian Parameter Estimation= ...PE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE662]], Spring 2010, Prof. Boutin
    2 KB (287 words) - 10:39, 20 May 2013
  • Implementation Issues Old Kiwi
    ...nces are not known, they can be estimated from the training set. Parameter estimation methods like maximum likelihood estimate or the maximum a posteriori estima ...te distance metric is very important. Distance metrics are used in density estimation methods (Parzen windows), clustering (k-means) and instance based classific
    2 KB (226 words) - 11:21, 7 April 2008
  • KNN Algorithm Old Kiwi
    The non-parametric density estimation is ...it belongs to that class. These points are known as nearest neighbors. The parameter k specifies the number of neighbors (neighboring points) used to classify o
    4 KB (637 words) - 08:46, 10 April 2008
  • Maximum Likelihood Estimation Old Kiwi
    ...PE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE662]], Spring 2010, Prof. Boutin # MLE is often simpler than other methods of parameter estimation.
    3 KB (465 words) - 10:37, 20 May 2013
  • MLE Examples: Exponential and Geometric Distributions Old Kiwi
    [[Category:parameter estimation]] =Examples of Parameter Estimation based on Maximum Likelihood (MLE): the exponential distribution and the geo
    3 KB (498 words) - 10:13, 20 May 2013
  • Maximum A Posteriori Old Kiwi
    ...that seeks parameter values that maximize the likelihood function for the parameter to calculate the best way of fitting a mathematical model to some data. Thi
    393 B (57 words) - 01:29, 7 April 2008
  • MLE Examples: Binomial and Poisson Distributions Old Kiwi
    [[Category:parameter estimation]] =Examples of Parameter Estimation based on Maximum Likelihood (MLE): the binomial distribution and the poisso
    2 KB (366 words) - 10:14, 20 May 2013
  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]],
    8 KB (1,337 words) - 08:44, 17 January 2013

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Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva