This condition is a direct consequence of the sampling process, ensuring that the aggregated probability density function of all sampled sets mirrors the overall density distribution of the neurons.

Probability Density Function Calculating probabilities for continuous random variables requires a different approach from the methods used with discrete variables. If all the outcomes of a continuous ...

A probability density function (PDF) describes the likelihood of different outcomes for a continuous random variable.

Here, two such methods, surrogate/model mixed ensemble (SMME) and Monte Carlo pattern/residual (MCPR), are developed and then are applied to construct joint probability density functions (PDFs) of ...

Representation: Typically represented using a joint probability density function (pdf), which describes the likelihood of the random variables falling within a particular range of values.

Then the ConvLSTM is utilized to capture the deep information of the input features. At last, the mixture density network capable of directly predicting probability density functions of loads is ...