• From eq.(3.4), we can set the sufficient statistic <math>t\left(x \right)</math> and parameters <span
    17 KB (2,590 words) - 09:45, 22 January 2015
  • where I is some open interval of the real number set <math>\mathbb{R}</math>. This is called a `parametric representation' of th ...to parametrize a graph described by y = f(x) is to choose a parameter and set it equal to x, then express y in terms of that parameter. For example, the
    10 KB (1,752 words) - 16:02, 14 May 2015
  • | <math> (i.e.,\ |a|R=\ the\ set\ of\ points\ {|a|z}\ for\ z\ in\ R)\ </math> | <math> R^{1/k}\ (i.e.,\ the\ set\ of\ points\ z^{1/k},\ </math>
    7 KB (1,018 words) - 07:55, 6 March 2015
  • *Definition of a set **[[Practice_Question_definition _set_ECE302S13Boutin|Do these signals form a set?]]
    7 KB (960 words) - 17:17, 23 February 2015
  • ...t Analysis is a process of extracting useful information from a noisy data set. Complex data sets may hid significant relationship between variables, and ...at a set frequency. We live in a three dimensional world, so we decide to set up three cameras in a rather random directions to collect the data on the m
    6 KB (1,043 words) - 11:45, 3 March 2015
  • Let <math class="inline">\left\{ t_{k}\right\}</math> be the set of Poisson points corresponding to a homogeneous Poisson process with param
    5 KB (780 words) - 00:25, 9 March 2015
  • This question is a set of short-answer questions (no proofs):
    5 KB (729 words) - 23:51, 9 March 2015
  • ...h> . The event space <math class="inline">\mathcal{F}</math> is the power set of <math class="inline">\mathcal{S}</math> , and the probability measure <m
    5 KB (735 words) - 00:17, 10 March 2015
  • ...ex as a function of <math class="inline">q</math> (the probability that a set of twins are identical.)
    5 KB (748 words) - 00:01, 10 March 2015
  • ...ent of' 'the set of real numbers' 'for all' n 'which are elements of' 'the set of natural numbers'. ...implies that <math>ln(x)</math> must be <math>< 0</math>), we arbitrarily set <math>s = -ln(x)</math> or <math>-s = ln(x)</math>, which leaves us with:
    3 KB (512 words) - 14:14, 1 May 2016
  • ...utilized the property that ''arctan(-x)'' = ''-arctan(x)''. Explicitly, I set a 'negative flag' if the ''arctan'' to be calculated had a negative argumen ...utput at a macro scale (ie not by counting assembly instruction cycles), I set the conditionals in the code such that the longest logical processing path
    8 KB (1,176 words) - 14:15, 1 May 2016
  • ...the 4 signs of the set of voltage equations, as well rule that bounds the set of possible sign configurations.
    3 KB (474 words) - 14:17, 1 May 2016
  • % set params
    2 KB (289 words) - 14:14, 1 May 2016
  • ...ydrogen and Carbon, it is redundant and needs to be removed. A sign that a set of vectors is a basis is that any vector you add to it from vector space V ...o generate every color and don't have any redundant colors among their own set, then they serve as a basis for all colors.
    14 KB (2,247 words) - 12:09, 3 March 2015
  • ...tes. Since <math>\phi</math> was allowed to be any scalar function, we can set <math>\phi = \theta</math> to obtain
    19 KB (3,027 words) - 11:01, 24 February 2015
  • ...late, then, that the transformation from one set of coordinates to another set is merely ...ansformation T, T is the Transformation from C1 to C2 and C2 is the second set of coordinates.
    18 KB (2,894 words) - 11:17, 3 March 2015
  • ...> <br /> where, <math>n</math> is the number of events (cardinality of the set <math>\{A_{i}\}</math>) in the sample space and, <math>i = 1, 2, ..., n</ma
    14 KB (2,241 words) - 09:42, 22 January 2015
  • ...e global maximum. So we take the derivative w.r.t. <math>\beta</math>, and set it equal to zero to find the maximum.
    13 KB (2,062 words) - 09:45, 22 January 2015
  • ...near separation, which is a hyperplane. The hyperplane can be defined by a set of points which are equidistant to <math>\mu_1</math> and <math>\mu_2</math
    19 KB (3,255 words) - 09:47, 22 January 2015
  • ...orthonormal transformations to convert the observed correlated data into a set of linearly uncorrelated data, which are the so-called principle components &nbsp; &nbsp; In this section, I will just use PCA on a simple data set to help you gain some intuition of PCA, the question of "why does it work"
    22 KB (3,459 words) - 09:40, 22 January 2015
  • ...atrix '''Σ'''. This is either known or it can be estimated from your data set. We assume that '''Σ''' is positive definite. We also assume wlog that ''' Then, '''Y''' is a set of <math>n</math> data points drawn from an uncorrelated bivariate Gaussian
    17 KB (2,603 words) - 09:38, 22 January 2015
  • ...="texhtml">θ</span>&nbsp;is a fixed, unknown constant<br>belonging to the set&nbsp;<math>\Theta \subset \mathbb{R}^{n}</math>. business of making predictions based on a set of solid assumptions, then we would be
    25 KB (4,187 words) - 09:49, 22 January 2015
  • ...hich means that the density function is estimated locally based on a small set of neighboring samples. Because of this locality, local (nonparametric) den
    15 KB (2,345 words) - 09:52, 22 January 2015
  • ...nbsp; Let D = {x<sub>1</sub>,x<sub>2</sub>,...,x<sub>N</sub>}&nbsp;to be a set of iid samples from the Gaussian distribution with μ and Σ unknown.&nbsp;
    7 KB (1,177 words) - 09:47, 22 January 2015
  • ...ndom variable X and the parameter θ can be vector-valued. Now we obtain a set of independent observations or samples S = {x<sub>1</sub>,x<sub>2</sub>,... ...he distribution p(θ ). Then the probability density function of X given a set of observations S can be estimated by<br>
    15 KB (2,273 words) - 09:51, 22 January 2015
  • ...f the most useful method to represent pattern classifiers is in terms of a set of '''''discriminant functions ''''' <math>g_i(\mathbf{x}), i=1,2,...,c</ma
    14 KB (2,287 words) - 09:46, 22 January 2015
  • ...the infinity. We set <math>P = \lim_{n \to \infty}P_n(e)</math>. Then, we set infinite sample conditional average probability of error as <math>P(e|x)</m ...in previous section can be utilized to get the upper error bound. First we set
    14 KB (2,313 words) - 09:55, 22 January 2015
  • ...nbsp;&nbsp; &nbsp; We divide the sample data into training set and testing set. Training data is used to estimate the model parameters, and testing data i Given a data set
    9 KB (1,382 words) - 09:47, 22 January 2015
  • Now, assume that a set of <math>N</math> independent samples were obtained from a certain class <m ...math>\theta</math> both the information in priori and the information from set <math>\mathcal{D}</math> of n samples <math>x_1, x_2, ... , x_n</math> need
    10 KB (1,625 words) - 09:51, 22 January 2015
  • ...e of discriminant functions as a means of classifying data. That is, for a set of classes <math> \omega_c </math>, we choose to classify a sample <math>\v
    16 KB (2,703 words) - 09:54, 22 January 2015
  • ...lambda \textbf{c}</math> also separates the data. One solution might be to set <math>|\textbf{c}|=1</math>. Another solution is to introduce a bias denote ...Other than the parameters in the optimization problem, the SVM has another set of parameters called hyperparameters, including the soft margin constant, <
    14 KB (2,241 words) - 09:56, 22 January 2015
  • ...' = 3. Column 4 - 7 gives the records counts with the threshold value''t'' set to the probability of current record. Accordingly, TPR and FPR can be compu
    11 KB (1,823 words) - 09:48, 22 January 2015
  • Given a set of training data in a region <math>\Re^n</math>, and a point <math>x_0</mat
    12 KB (2,086 words) - 09:54, 22 January 2015
  • ...iven as <math>(x_i,y_i)</math> where i is the index number of the training set, and <math>x_i</math> is hair length in inches and <math>y_i=1</math> indic If we set the above equation to zero, there is no close form solution, so we are goin
    9 KB (1,540 words) - 09:56, 22 January 2015
  • # Prior selection : How to set priors?
    18 KB (2,852 words) - 09:40, 22 January 2015
  • Therefore, when both of the parameters are unknown for a set of Gaussian distributed random vector, the ML estimator of covariance matri
    19 KB (3,418 words) - 09:50, 22 January 2015
  • ...ith corresponding labels <math> y_{1}, y_{2}, ..., y_{N} </math> from some set of <math> C </math> classes <math> w_1,w_2,...w_C</math>. Together these are often called the training set, because they are used to "train" the classifier.
    9 KB (1,604 words) - 09:54, 22 January 2015
  • The KNN method begins with a set of labeled training data (points) in the feature space. Note that it is ess <br> Number of total points in the training set: <span class="texhtml">''N''</span> <br>
    6 KB (1,013 words) - 09:55, 22 January 2015
  • ...um value of likelihood, which means is the most likely to observe the data set samples.<br> &nbsp; Suppose we have a set of n independent and identically destributed observation samples. Then dens
    13 KB (1,966 words) - 09:50, 22 January 2015
  • =<math>\Sigma^{-1}\sum\limits_{j=1}^{N}(x_j-\mu)</math> set to be 0 \end{bmatrix}</math> set to be 0
    11 KB (2,046 words) - 09:51, 22 January 2015
  • = Set up and Tricks = There are a number of tricks that are used in the set up for LDA. The first and most important is the concatenation trick. This t
    10 KB (1,666 words) - 09:56, 22 January 2015
  • ...l wish to discover some form of underlying structure in an unlabelled data set. Such a task falls under the category of unsupervised learning. ...points in multidimensional space. For example, consider the following data set:
    8 KB (1,350 words) - 09:57, 22 January 2015
  • ...series which may or may not converge. The domain for this function is the set of all ''x'''s for which the series converges.
    9 KB (1,632 words) - 17:19, 27 February 2015
  • The discrete-time Fourier transform (DTFT) of a discrete set of real or complex numbers x[n] with n=all integers, is a Fourier series, w
    3 KB (515 words) - 19:02, 16 March 2015
  • The result of this function is a set of time-shifted impulses whose amplitudes match those of the input signal x
    4 KB (734 words) - 17:56, 16 March 2015
  • ...above equation we can see that if we were to have <math>{\omega}</math> be set to <math>{\omega}_{o}</math> we get
    5 KB (862 words) - 19:02, 16 March 2015
  • Using the Nyquist condition to set the sampling period <math>T</math>:<br><math>\frac{1}{T} > 2f_M \ or \ T <
    10 KB (1,650 words) - 18:04, 16 March 2015
  • ...ed signal. Essentially, the comb is grabbing points on the graph x(t) at a set interval, T. <math>
    2 KB (335 words) - 08:57, 14 March 2015
  • ...a specific <math>\theta</math>. Repeating over many <math>\theta</math>, a set of data like that shown in Figure 2 will be obtained. All the data will be
    8 KB (1,252 words) - 18:26, 9 February 2015
  • Let <math class="inline">\left\{ t_{k}\right\}</math> be the set of Poisson points corresponding to a homogeneous Poisson process with param
    4 KB (700 words) - 16:48, 13 March 2015

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