In this paper we discuss non-parametric estimation of the probability density function (PDF) of a univariate random variable. This problem has been the subject of a vast amount of scientific ...

Learn how to use kernel density estimation, a technique that approximates the probability density function of a random variable in machine learning, and how to choose the best kernel function and ...

Probability density function is a statistical expression defining the likelihood of a series of outcomes for a continuous variable, such as a stock or ETF return.

The construction of a confidence interval for unknown probability density function (pdf) trough histogram for the first time has been suggested by Smirnov [1]. Bikel and Rosenblatt [2], Rosenblatt [3] ...

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the ...

By using one of the common stock probability distribution methods of statistical calculations, an investor may determine the likelihood of profits from a holding.