Understanding disease progression is crucial for detecting critical transitions and finding trigger molecules, facilitating early diagnosis interventions. However, the high dimensionality of data and ...

In the early 1970s, statisticians had difficulty in analysing data where the random variation of the errors did not come from the bell-shaped normal distribution. Besides normality, these traditional ...

Latent Gaussian models (LGMs) are a staple in the statistical modeling toolkit, especially valuable when dealing with data that exhibits complex, hidden patterns. For engineers, think of LGMs as the ...

Most of the latest studies on detection models for DoS or DDoS have been applied in general networks. Therefore, no dataset of DoS or DDoS in electric vehicle (EV) charging infrastructure exists. In ...

We derive blending coefficients for the optimal blend of multiple independent prediction models with normal (Gaussian) distribution as well as the variance of the final blend. We also provide lower ...

Today, I am going to talk about deviations from normality. In other words, we are going to argue that actual time series returns on different asset classes are actually not normally distributed in ...

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high ...

Abstract: Geometric reconstruction of opaque surfaces from images is a longstanding challenge in computer vision, with renewed interest from volumetric view synthesis algorithms using radiance fields.

A new algorithm is suggested based on the central limit theorem for generating pseudo-random numbers with a specified normal or Gaussian probability density function. The suggested algorithm is very ...

Thompson Sampling is an algorithm that can be used to analyze multi-armed bandit problems. Imagine you're in a casino standing in front of three slot machines. You have 10 free plays. Each machine ...