Statistics and Machine Learning Toolbox™ offers several ways to work with the normal distribution.
Create a probability distribution object
NormalDistributionby fitting a probability distribution to sample data or by specifying parameter values. Then, use object functions to evaluate the distribution, generate random numbers, and so on.
Work with the normal distribution interactively by using the Distribution Fitter app. You can export an object from the app and use the object functions.
Use distribution-specific functions with specified distribution parameters. The distribution-specific functions can accept parameters of multiple normal distributions.
To learn about the normal distribution, see Normal Distribution.
|Normal probability distribution object|
NormalDistribution Object Functions
|Cumulative distribution function|
|Gather properties of Statistics and Machine Learning Toolbox object from GPU (Since R2020b)|
|Inverse cumulative distribution function|
|Interquartile range of probability distribution|
|Mean of probability distribution|
|Median of probability distribution|
|Negative loglikelihood of probability distribution|
|Confidence intervals for probability distribution parameters|
|Probability density function|
|Plot probability distribution object (Since R2022b)|
|Profile likelihood function for probability distribution|
|Standard deviation of probability distribution|
|Truncate probability distribution object|
|Variance of probability distribution|
Maximum Likelihood Estimation
- Normal Distribution
Learn about the normal distribution. The normal distribution is a two-parameter (mean and standard deviation) family of curves. Central Limit Theorem states that the normal distribution models the sum of independent samples from any distribution as the sample size goes to infinity.