Continuous Distributions
Compute, fit, or generate samples from real-valued distributions
连续概率分布是随机变量可以假设任何值的概率分布。统计和机器学习工具箱™提供了几种使用连续概率分布的方法,包括概率分布对象,命令行功能和交互式应用程序。有关这些选项的详细信息,请参阅Working with Probability Distributions.
- 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分布
Evaluate and generate random samples from chi-square distribution - 指数分布
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 - 伽玛分布
Fit, evaluate, and generate random samples from gamma distribution - Generalized Extreme Value Distribution
从广义极值分布中拟合,评估和生成随机样本 - 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
根据内核功能拟合平滑的分布,并评估分布 - 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 - Nakagami Distribution
Fit, evaluate, and generate random samples from Nakagami distribution - 非中央奇广场分布
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 - 学生的T分配
评估和生成学生的T分布的随机样本 - 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)
从连续均匀分布评估和产生随机样品 - Weibull Distribution
FIT,评估和生成来自Weibull分布的随机样本