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# 커널 분포

커널 함수를 기반으로 하여 평활화된 분포 피팅 및 분포 실행

## 함수

 `fitdist` 데이터에 확률 분포 객체 피팅하기 `distributionFitter` Open Distribution Fitter app `ksdensity` 일변량 데이터와 이변량 데이터에 대한 커널 평활화 함수 추정값 `mvksdensity` Kernel smoothing function estimate for multivariate data
 `cdf` 누적 분포 함수 `icdf` Inverse cumulative distribution function `iqr` Interquartile range `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative loglikelihood of probability distribution `pdf` 확률 밀도 함수 `random` 난수 `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution

## 객체

 `KernelDistribution` Kernel probability distribution object

## 도움말 항목

Kernel Distribution

A kernel distribution is a nonparametric representation of the probability density function of a random variable.

Nonparametric and Empirical Probability Distributions

Estimate a probability density function or a cumulative distribution function from sample data.

Fit Kernel Distribution Object to Data

This example shows how to fit a kernel probability distribution object to sample data.

Fit Kernel Distribution Using ksdensity

This example shows how to generate a kernel probability density estimate from sample data using the `ksdensity` function.

Fit Distributions to Grouped Data Using ksdensity

This example shows how to fit kernel distributions to grouped sample data using the `ksdensity` function.