Probability distribution functions (PDF) are sometimes known as probability densities. In the case of continuous distributions, the area under the curve of the PDF for each interval equals the probability for the random variable to fall within that interval. The PDFs are useful in calculating event probabilities, characteristic functions and moments of a distribution.

Igor Function Probability Distribution
StatsBetaPDF Beta
StatsBinomialPDF Binomial
StatsCauchyPDF Cauchy
StatsChiPDF Chi-squared
StatsDExpPDF Double-exponential
StatsErlangPDF Erlang
StatsErrorPDF Error
StatsEValuePDF Extreme-value (type I, Gumbel)
StatsExpPDF Exponential
StatsGammaPDF Gamma
StatsGeometricPDF Geometric
StatsHyperGPDF Hyper-geometric
StatsLogNormalPDF Lognormal
StatsMaxwellPDF Maxwell
StatsNBinomialPDF Negative binomial
StatsNCChiPDF Non-central Chi-squared
StatsNCFPDF Non-central F
StatsNCTPDF Non-central Student T
StatsNormalPDF Normal (Gaussian)
StatsParetoPDF Pareto
StatsPoissonPDF Poisson
StatsPowerPDF Power
StatsRayleighPDF Rayleigh
StatsRectangularPDF Uniform
StatsStudentPDF Student T
StatsTriangularPDF Triangular
StatsVonMisesPDF von-Mises
StatsWaldPDF Wald
StatsWeibullPDF Weibull




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