Software Listing: 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 normal distribution curve sigma : standard deviation of the normal distribution curve (hint: normal dist mean=0, sigma=1) plotting: Plot the calculated area if plotting = 1 Output: area under the curve. Example: x=[-20:20] % your data points sigma=length(x)/2/3.5 % PDF width is 3.5 sigma mean=0 % mean between -20 and 20 normaldistribution(0, mean, sigma,1) % Calculate area from -inf to 0 Author: Sherif Omran University and university hospital of Zurich Date: May 2009 Part of my phd thesis: email: sherif.

  • Platform: Matlab, Scripts

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.

  • Platform: Matlab, Scripts

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 1 Output: x, an approximation for the inverse normal at u Reference: Pau Glasserman, Monte Carlo methods in financial engineering, vol. 53 of applications of Mathematics (New York), Springer-Verlag, new York, 2004, p.67-68.

  • Platform: Matlab, Scripts

Two functions are included. The first is 'quadriorth.m' which calculates the orthants probabilities of the quadrivariate normal distribution. Imagine a 4-dimensional space divided into sub-spaces according to the sign of each variable, which results in 2^4 = 16 orthants, say + + + +, + + + , + + +, + + +, ..., and . This function gives the probability content in each of these 16 orthants. The method is based on a recent paper by Sinn and Keller (2010) which reduces the 4-dimensional integral to the sum of four one-dimensional ones (which can be treated numerically as one integral). Similarly, the function 'pentaorth.

  • Platform: Matlab, Scripts

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) determines normalization for covariance 0 : Normalizes by NUM-1. This makes cov(R) the best unbiased estimate of the covariance matrix (Default) 1 : Normalizes by NUM and produces the second moment matrix of the observations about their mean. MU : Either a 1-by-D row vector, or a scalar across dimensions.

  • Platform: Matlab, Scripts
  • BallDrop
  • License: Freeware
  • Price: 0.00
  • Rating

BallDrop is a multi-platform compatible script that displays tiny balls that drop to form a normal distribution. Source code available..

  • Platform: JavaScript, Scripts

Let X(1),X(2),...,X(N) be the ordered statistics of a sample X1,X2,...,XN from a normal distribution. Let M = median([X1,X2,...,XN]) and S = mad([X1,X2,..XN],1)/.6745 be robust stimators of (respectively) the mean and standard deviation of the distribution. The Hampel identifier is a rule which identifies as outliers all values of the sample X satisfying |X-M|/S > g(N,alpha). The function g(N,alpha) serves for standardizing the identifier in the following way (see [1], p. 783, standardization (4)): P(no outliers in the sample) = P(|X(N)-M)|/S < g(N,alpha)) = 1 - alpha. This matlab function calculates the critical values, i.

  • Platform: Matlab, Scripts

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 S is treated as a standard deviation. R = RCR(S,P) computes the semi-edge-length of the hypercube with probability P instead of the default, which is 0.95. R is the two-tailed, equicoordinate quantile corresponding to P. The hypercube edge-length is 2*R. R = RCR(S,P,NP) uses NP quadrature points instead of the default, which is 2^11.

  • Publisher: Tom Davis
  • Date: 15-03-2013
  • Size: 82 KB
  • Platform: Matlab, Scripts

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 algorithm, i.e. a classical accept-reject algorithm using several proposal distributions, each one being adapted to the different values of the distribution parameters. Then, with respect to these parameter values, the proposal distribution which gives the highest average probability of acceptance is used to simulate a variable.

  • Platform: Matlab, Scripts

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 ) * resolution, where Z is an unobservable vector of independent standard normal errors. Based on this Fiducial Distribution, DigitFD estimates the confidence intervals for the parameter mu and sigma. Syntax: result = DigitFD(measurements) result = DigitFD(measurements,options) INPUTS: measurements - vector of the digitized measurements; options - options structure OUTPUT: result - results structure with the fields: Resolution Measurements MeanMeasurements StdMeasurements NumberOfMeasurements NumberOfDifferentValuesInMeasurements...

  • Platform: Matlab, Scripts

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 biased by rare events, zero abundances can be included if desired, it more accurately measures known long term variation from short time series and unlike CV / SDL it doesn't artificially suggest spectral reddening. See Heath, J.P. 2006. Quantifying temporal variability in population abundances. Oikos, 115:573-581.

  • Publisher: Joel Heath
  • Date: 15-02-2013
  • Size: 10 KB
  • Platform: Matlab, Scripts

The Logistic-Normal distribution [1] 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 [2]. Instructions: 1. First make sure you have the Matlab Statistics Toolbox. 2. Unzip hlnfit.zip. 3. Open Matlab and run hlnscript See also: http://www.cs.cmu.edu/~jch1/research/hln/hlnfit.html [1] J Aitchison and S.M. Shen, Logistic-normal distributions: Some properties and uses,Biometrika 67 (1980). [2]Peter Hoff, Nonparametric modelling of hierarchically exchangeable data, Tech.

  • Platform: Matlab, Scripts

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.

  • Publisher: Peter Dunn
  • Date: 14-05-2013
  • Size: 10 KB
  • Platform: Matlab, Scripts

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 not yet include the option to have different sigmas for the x- and y- axis, feel free to add it if you need. There might be some bugs in the code, but for the purpose I have used it it works fine. Pls let me know if there is anything not working properly, and I'll correct it..

  • Platform: Matlab, Scripts
  • decolog
  • License: Freeware
  • Price: 0.00
  • Rating

DECOLOG develops is able to decode the information present in the natural mixture of particles/sediments by mixture of the log-normal distribution. That is with innovative techniques of optimization no needs of initial guessing the observed distribution
decolog License - Public Domain.

  • Platform: WinOther
  • t1mBR
  • License: Freeware
  • Price: 0.00
  • Rating

is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many wejority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text.

  • Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x

Big Buttons Keyboard Deluxe - BETA

** Attn devs - the Delta II matrix is meant to be licensed - please see below **

** This is a beta/proof-of-concept release of Big Buttons Keyboard - not meant for normal distribution.**
** It will appear and disappear from the Play Store.**

Please report any problems to: beta@chicagologic.com


Big Buttons Keyboard Beta

Want to avoid those tiny, frustrating buttons on your smartphone keyboard?

Then it's time to enjoy the most advanced smartphone keyboard layout in the world.

Big Buttons Keyboard also has bigger number & punctuation buttons, making EVERYTHING you type on your smartphone EASIER and more ACCURATE!

But the MAGIC is in the patented Delta II modified-QWERTY keyboard layout that is surprisingly FAST, ACCURATE & EXTREMELY quick-to-learn!!

A more powerful version of...

  • Platform: Android 1.x, Android 2.x, Android 3.x, Android 4.4, Android 4.x

Do you want to see how random numbers from different probability distribution are generated? The Random Numbers Generator and Statistics Set can show you how. It contains practical and well explained examples of: 1. Log Normal Distribution 2. Log Pearson Type III Distribution 3. Normal Distribution 4. Chi-Square Distribution 5. F-Distribution 6. Student-T Distribution 7. Multivariate Standard Normal Distribution8. Gamma Distribution 9. Beta Distribution 10. Hypergeometric Distribution 11. Triangular Distribution12. Binomial Distribution. Excel VBA Models - Home. Affordable learning tools in advanced Excel VBA modeling in finance, statistics, and mathematics through our VBA source code tutorials.

  • Platform:

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..

  • Publisher: S B
  • Date: 14-03-2013
  • Size: 10 KB
  • Platform: Matlab, Scripts
  • z2p
  • License: Shareware
  • Rating

Converts normally distributed z-statistic to one- or two-tailed p-value by 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 distributed z-statistic (positive or negative) (optional) tailed: the number of tails over which to compute the probability value. (Note: by symmetry of the normal distribution, the two-tailed p-value is twice the one-tailed value.) This function is useful for hypothesis testing when the test statistic is normally distributed.

  • Platform: Matlab, Scripts

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