Continuous Distributions
Compute, fit, or generate samples from real-valued distributions
A continuous probability distribution is one where the random variable can assume any value. Statistics and Machine Learning Toolbox™ offers several ways to work with continuous probability distributions, including probability distribution objects, command line functions, and interactive apps. For more information on these options, see Working with Probability Distributions.
Categories
- Beta Distribution
Fit, evaluate, and generate random samples from beta distribution
- Birnbaum-Saunders Distribution
Fit, evaluate, and generate random samples from Birnbaum-Saunders distribution
- Burr Type XII Distribution
Fit, evaluate, and generate random samples from Burr type XII distribution
- Chi-Square Distribution
Evaluate and generate random samples from chi-square distribution
- Exponential Distribution
Fit, evaluate, and generate random samples from exponential distribution
- Extreme Value Distribution
Fit, evaluate, and generate random samples from extreme value distribution
- F Distribution
Fit, evaluate, and generate random samples from F distribution
- Gamma Distribution
Fit, evaluate, and generate random samples from gamma distribution
- Generalized Extreme Value Distribution
Fit, evaluate, and generate random samples from generalized extreme value distribution
- Generalized Pareto Distribution
Fit, evaluate, and generate random samples from generalized Pareto distribution
- Half-Normal Distribution
Fit, evaluate, and generate random samples from half-normal distribution
- Inverse Gaussian Distribution
Fit, evaluate, and generate random samples from inverse Gaussian distribution
- Kernel Distribution
Fit a smoothed distribution based on a kernel function and evaluate the distribution
- Logistic Distribution
Fit, evaluate, and generate random samples from logistic distribution
- Loglogistic Distribution
Fit, evaluate, and generate random samples from loglogistic distribution
- Lognormal Distribution
Fit, evaluate, generate random samples from lognormal distribution
- Loguniform Distribution
Evaluate and generate random samples from loguniform distribution
- Pearson Distribution
Evaluate Pearson distribution probability functions and generate random samples
- Nakagami Distribution
Fit, evaluate, and generate random samples from Nakagami distribution
- Noncentral Chi-Square Distribution
Evaluate and generate random samples from noncentral chi-square distribution
- Noncentral F Distribution
Evaluate and generate random samples from noncentral F distribution
- Noncentral t Distribution
Evaluate and generate random samples from noncentral t distribution
- Normal Distribution
Fit, evaluate, and generate random samples from normal (Gaussian) distribution
- Piecewise Linear Distribution
Evaluate and generate random samples from piecewise linear distribution
- Rayleigh Distribution
Fit, evaluate, and generate random samples from Rayleigh distribution
- Rician Distribution
Fit, evaluate, and generate random samples from Rician distribution
- Stable Distribution
Fit, evaluate, and generate random samples from stable distribution
- Student's t Distribution
Evaluate and generate random samples from Student’s t distribution
- t Location-Scale Distribution
Fit, evaluate, and generate random samples from t location-scale distribution
- Triangular Distribution
Evaluate and generate random samples from triangular distribution
- Uniform Distribution (Continuous)
Evaluate and generate random samples from continuous uniform distribution
- Weibull Distribution
Fit, evaluate, and generate random samples from Weibull distribution
