Statistics and Machine Learning Toolbox™ offers several ways to work with the normal distribution.
Create a probability distribution object
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
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|
|Cumulative distribution function|
|Gather properties of Statistics and Machine Learning Toolbox object from GPU|
|Inverse cumulative distribution function|
|Mean of probability distribution|
|Median of probability distribution|
|Negative loglikelihood of probability distribution|
|Confidence intervals for probability distribution parameters|
|Probability density function|
|Profile likelihood function for probability distribution|
|Standard deviation of probability distribution|
|Truncate probability distribution object|
|Variance of probability 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.