Abstract: Traditionally, the uncertainty qualification is utilized with the known probability distribution function (PDF). However, in some scenarios, the PDFs of some uncertain variables are modeled ...

Research from the Netherlands' Delft University of Technology (TU Delft) has resulted in a probabilistic framework to predict the energy yields of fleets of residential solar PV plants. The novel ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

Abstract: In this paper, we propose a model of a parameter estimator filter for Black Box-type Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its intrinsic ...

The mathematician Daniel Litt has driven social media users to distraction with a series of simple-seeming but counterintuitive probability puzzles. In late January, Daniel Litt posed an innocent ...

What Is A Probability Density Function? A probability density function, also known as a bell curve, is a fundamental statistics concept, that describes the likelihood of a continuous random variable ...

Quantum annealing (QA) can be competitive to classical algorithms in optimizing continuous-variable functions when running on appropriate hardware, show researchers from Tokyo Tech. By comparing the ...