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In statistics, kernel density estimation (or Parzen window method, named after Emanuel Parzen) is a non-parametric way of estimating the probability density function of a random variable. As an illustration, given some data about a sample of a population, kernel density estimation makes it possible to extrapolate the data to the entire population.

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Although less smooth density estimators such as the histogram density estimator can be made to be asymptotically consistent, others are often either discontinuous or converge at slower rates than the kernel density estimator. Rather than grouping observations together in bins, the kernel density estimator can be thought to place small "bumps" at each observation, determined by the kernel function. The estimator consists of a "sum of bumps" and is clearly smoother as a result (see below image).

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Просто я раньше не особо видел вообще такую штуку.

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В ответ на:

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В любом случае, нужно понять, зачем автору вообще плотность, что он будет с ней делать?

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В ответ на:

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Плотность мне не нужна, мне нужен график, где по оси X - время, по оси - Y - вероятность

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