When normed=True, the counts can be interpreted as pdf values: counts, bin_edges = np.histogram(a, bins=num_bins, normed=True) The cdf is given by dx = bin_edges[1]-bin_edges[0] cdf = np.cumsum(counts*dx) The distance between the bin edges is uniform, so dx is constant. counts*dx gives the probability mass for each bin. Now np.cumsum of...

matlab,probability,truncate,cdf

I would suggest first using the Matlab truncate function to adjust your distribution: pd = makedist('poiss') trunc = truncate(pd,1,3) for Poisson, it can only be positive. set a discrete range: x = 0:.1:4; distribution = pdf(trunc,x); cummulative = cdf(trunc,x); alternatively, you could integrate the pdf function using matlab integrate...

By definition, the cumulative distribution function F_a for an attribute a is defined by F_a(x) = # documents with attribute value <= x / # of documents So you can compute the CDF with F_a(x) = db.collection.count({ "a" : { "lte" : x }) / db.collection.count({ "a" : { "$exists"...

Watch out - you want to save the sign of x-mu, not just of x: int sign = 1; if (x < mu) sign = -1; x = fabs(x-mu)/sqrt(2.0*sigma*sigma); Otherwise your scaling is correct....

Try this: def hypergeometricCDF(N,K,n,x): """ Call: p = hypergeometricCDF(N,K,n,x) Input argument: N: integer K: integer n: integer x: integer Output argument: p: float Example: hypergeometricCDF(120,34,12,7) => 0.995786 """ k = arange(x+1) p = sum(exp(log_hypergeometricPMF(N,K,n,k))) return(p) log_hypergeometricPMF is defined on top of the file ;)...

There is no need to use two plots commands, just use the pointinterval option: plot 'data' pointinterval 5 with linespoints That plots every line segment, but only every fifth point symbol. The big advantage is, that you can control the behaviour with set style line: set style line 1 lc...

Your code (and conclusion) look correct to me. It might be graphically better to use type="h" to draw a "high-density" plot; this makes it clearer that there is zero probability for non-integer values of x. x <- 1:50 par(las=1,bty="l") ## cosmetic plot(x,dbinom(x ,size = 50,prob = 0.513),type="h", ylab="PMF", main="Binomial Distribution...

You can use uniroot(...) for this. [Note: If the point of this exercise is to implement your own version of a Newton Raphson technique, let me know and I'll delete the answer.] If I'm understanding this correctly, you want to generate random samples from a distribution with probability density function...

statistics,wolfram-mathematica,normal-distribution,cdf

1) MultinormalDistribution is now built in, so don't load MultivariateStatistics it unless you are running version 7 or older. If you do you'll see MultinormalDistribution colored red indicating a conflict. 2) this works: sig = .5; u = .5; dist = MultinormalDistribution[{0, 0}, sig IdentityMatrix[2]]; delta = CDF[dist, {xx, yy}]...

python,statistics,scipy,normal-distribution,cdf

edit: you actually need import norm from scipy.stats. I found the answer. You need to use ppf in scipy.stats which stands for "percent point function". So let's say you have a normal distribution with stdDev = 1, and mean = 0 and you want to find the value at which...

It is unclear why you need N, Ndft and X.length(). Aren't one value to be enough? As mentioned by someone, in integer division j/N you're getting zeroes. Please make it to be double(j)/double(N) ...

GNU Scientific Library can do that, and it's a plain C library available on pretty much any system. From the documentation: Function: double gsl_cdf_lognormal_P (double x, double zeta, double sigma) Function: double gsl_cdf_lognormal_Q (double x, double zeta, double sigma) Function: double gsl_cdf_lognormal_Pinv (double P, double zeta, double sigma) Function: double...

Try findInterval, e.g.: findInterval(c(0.1, 0.98), cdf) + 1 # [1] 1 7 ...

Two possible issue with this algorithm. Handling large values of v. When v becomes large, we should recover the standard normal distribution. However, you have a while loop over v. So v=1000000 say, becomes slow Tail accuracy. How does the algorithm cope in the extreme tails? typically, we need to...

Combining the example by @Robert and code from the answer featured here: How to get a reversed, log10 scale in ggplot2? library("scales") library(ggplot2) reverselog_trans <- function(base = exp(1)) { trans <- function(x) -log(x, base) inv <- function(x) base^(-x) trans_new(paste0("reverselog-", format(base)), trans, inv, log_breaks(base = base), domain = c(1e-100, Inf)) }...