Kernel density estimation (KDE) is a technique that can help you estimate the probability density function (PDF) of a random variable in machine learning (ML). PDFs are useful for describing the ...

First, a sliding window strategy is adopted to convert one-dimensional (1D) data into two-dimensional (2D) matrices to reconstruct input features. Then the ConvLSTM is utilized to capture the deep ...

Example of a Probability Density Function (PDF) The probability density function measures continuous variables. Having said that, it's important to note that stock and investment returns are ...

#density1d.grid() Returns the internal grid array of total accumulated density values per bin. To instead produce an array of objects containing coordinate values and probability density function ...

A novel strategy of probability density function (PDF) shape control is proposed in stochastic systems. The controller is designed whose parameters are optimally obtained through the improved particle ...

This Python code simulates a 2D quantum wave packet encountering a potential barrier using the Finite Difference Time Domain (FDTD) method. The simulation computes the time evolution of the wave ...

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 ...