Probability Density Function Estimation Sample Clauses

Probability Density Function Estimation. Mutual Information takes as input the pdfs, p(x), p(y) and p(x, y) as shown in equation (1). Histograms were used for pdf estimation. A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Xxxx Xxxxxxx [21]. To construct a histogram, the first step is to "Bin" the range of values—that is, divide the entire range of values into a series of small intervals— and then count how many values fall into each interval. Basically the value range of the X and Y random variables is divided into R segments, with R being the number of Bins of the histogram. Then each value of the time series is classified into one of the R Bins ant the value of that Bin is incremented by 1. For p(x, y) the result is a 2-D array and the pair of X, Y values, each timestamp, is classified in one of the RxR Bins and its value is incremented by 1. The result is the estimated pdfs for X and Y and X, Y. There exist more methods for pdf estimation like Kernel Density Estimation (KDE) or K-Nearest Neighbour (KNN) that will be considered at the next stage of implementation for the MI algorithm.
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Related to Probability Density Function Estimation

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