Understanding Normal Distribution
This function calculates the probability under the normal distribution curve, plots the graph and the area calculated.Normaldistribution calculating the area under a normal distribution curve from -ve infinity upto point x. Input: x : point on the normal distribution curve mean : mean of the...
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Forces the pdf of data to have a normal distribution using a data adaptive lookup table. [normX,Bx,By]=normalizepdf(X)normX=N(X), where N is an data adaptive monotonically increasing function. normX will have zero mean and unit variance.Bx,By is the lookup table
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Applying the inverse transform method to the normal distribution entails evaluation of the inverse normal. This is the Beasley-Springer-Moro algorithm for approximating the inverse normal.Input: u, a sacalar or matrix with elements between 0 and 1Output: x, an approximation for the inverse normal...
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MVNRND2 Random vectors from the multivariate normal distribution. R = MVNRND2(MU,SIGMA,NUM) returns a NUM-by-D matrix R of multivariate normal random vectors whose mean and covariance matrix match the given input parameters, MU (1-D vector) and SIGMA (D-by-D matrix) [...] = MVNRND2(...,COVNORM)...
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R = RCR(S) computes the semi-edge-length of the mean-centered hypercube with 95% probability given S, which is either a covariance matrix or a vector of standard deviations from a multivariate normal distribution. If S is a real, nonnegative vector, RCR(S) is equivalent to RCR(DIAG(S.^2)). Scalar...
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This function generate random variables distributed according to a truncated normal distribution (or, by a translation, to a normal distribution with positive support). This kind of problem is especially interesting for generating variables with MCMC methods.We use a mixed accept-reject...
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DigitFD generates random sample from the fiducial distribution of the parameters mu and sigma [of the unobservable normal distribution], based on the (digitized) measurements from instrument with limited, however known resolution. Here,measurements = round( (mu + sigma * Z)/resolution ) *...
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This technique simply calculates variation based on a comparison of all abundances in a time series, and is free of assumptions of an underlying 'normal' distribution. It is more robust that the coefficient of variation CV and/or the standard deviation of log transformed abundances SDL, it is not...
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The Logistic-Normal distribution  is a distribution over a simplex which forms a richer class of distributions than Dirichlets and better captures intercomponent correlations. We present the method for fitting a Hierarchical Logistic-Normal (HLN) distribution given by Hoff .Instructions:1....
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The function bvnormal supplies a bivariate normal distribution for the user to interact with. The user can rotate the picture for better views and adjust the parameters of the distribution. A good teaching tool enabling students to better visualize the bivariate normal
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This is a small program that creates a normalized 2-dimensional normal distribution, also known as a gaussian bell. The user has the options to decide the center of the distribution, the standard deviation, the size of the output matrix, and the area over which to create the distribution.I did...
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Easily convert from one metric to another (e.g. scaled scores to percentiles), see which range they fall into and see the scores on a normal distribution curve. Useful age calculator and stopwatch for use whilst testing. Requirements: iOS 6.0 or later. Compatible with iPhone, iPad, and iPod...
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Updates ver 1.1 --------------- Made memory adjustments to support new devices for both 7.1 and 8.0 Shake It Share It is a handy pictures app to surprise you with your random pictures in your camera roll. It uses a normal distribution randomising algorithm to surprise you with pictures in your...
Platforms: Windows Phone
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Here is the code for the pricing of an american call option with one dividend. This is the Roll, Geske,Whaley approximation of an AMerican call with one dividend. This code makes use of Bivariate normal distribution and normal distribution. More pricing options would be followed soon.
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Converts normally distributed z-statistic to one- or two-tailed p-valueby integrating the standard normal pdf. If no "tails" value is specified, z2p computes the two-tailed value by default. The output p is the same size as z, which can be a scalar, vector, or matrix. Inputs:z: normally...
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% pearspdf% [p,type,coefs] = pearspdf(X,mu,sigma,skew,kurt)%% Returns the probability distribution denisty of the pearsons distribution% with mean `mu`, standard deviation `sigma`, skewness `skew` and% kurtosis `kurt`, evaluated at the values in X.%% Some combinations of moments are not valid for...
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Fits the Laird-Ware Linear Random Effects Model. This model assumes that for each subject y=x*b+z*g+e where x and z are known m x p and m x r matricies, b is p x 1 parameter vector and g is a r vector which has a multivariate normal distribution with mean zero, e is a vector of identitcal...
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Performs watermarking of the input image by decomposing the image using haar wavlet. Noise generated with normal distribution is used as the key and is added to the input image to obtain the watermarked image.
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The result will be displayed in a matrix format.For uniform distributions,the values returned by the MATLAB function rand() are multiplied by (b-a), then added to a.For normal distributions the output of randn() is multiplied by the standard deviation (b), and then added to the desired to the...
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In statistics, confidence intervals can be used to express the degree to which sample results are reliable. Confidence intervals are often stated in terms of a percentage like 90% or 95%. When the sample or population distribution is the Normal (bell-shaped) distribution, the confidence...
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