The increasing diversity of scientific and engineering data has driven the development of flexible techniques for inferring probability distributions without assuming a specific parametric family.

Abstract: Kernel density estimation (KDE), a flexible nonparametric technique unconstrained by specific data distribution assumptions, is extensively employed in fault modeling. However, its ...

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As municipalities encourage more housing density, neighbors debate what kind of construction should be allowed. By Alexander Nazaryan It was the third floor that did it for Courtney Leonard. For ...

Hall County commissioners are set to vote later this week on a resolution that would extend the county's existing suspension of high-density residential rezonings, as well as suspend any potential ...

SEATTLE--(BUSINESS WIRE)--Sabey Data Centers, a leading designer, builder and operator of multi-tenant data centers, announces today an expanded partnership with JetCool, a Flex company and innovator ...

If you’re going online to buy some last-minute gifts this holiday season, there’s a chance the price you pay will be influenced by what’s known as “surveillance pricing.” Some retailers are using ...

However, NGD faces several challenges associated with gamma-ray generation and attenuation complexities. Unlike GGD, which utilizes 0.662 MeV monoenergetic γ rays from a 137 Cs source, NGD employs ...

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology ...

Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

Set create_data and do_plotting to True and press play. Initiation is at the bottom of the script. Requires numpy, matplotlib, and scipy. adk_estimator: Contains the functions that does the adaptive ...