Artificial intelligence (AI) applied to abdominal imaging can help predict adults at higher risk of falling as early as ...

The US Commerce Department's Bureau of Industry and Security (BIS) has shifted its license review policy for exports of ...

Abstract: Bayesian Network is a significant graphical model that is used to do probabilistic inference and reasoning under uncertainty circumstances. In many applications, existence of discrete and ...

ABSTRACT: Since the pioneering work of Markowitz on portfolio theory in 1950s, numerous developments have advanced to improve the original technique of portfolio optimisation. Current research on the ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Add a description, image, and links to the conditional-density-function topic page so that developers can more easily learn about it.

Bayesian statistics remain popular for addressing inverse problems, whereby quantities of interest are determined from their noisy and indirect observations. Bayes’ theorem forms the foundation of ...

In this paper, we consider the function f p ( t )= 2p X 2 ( 2p t+p;p ) , where χ²(x; n) defined by X 2 ( x;p )= 2 −p/2 Γ( p/2 ) e −x/2 x p/2−1 , is the density function of a χ²-distribution with n ...

Abstract: The Vlasov-Maxwell equations describe the coupled evolution of collisionless plasma particle distribution function (PDF) and the electromagnetic field. The system is exceedingly multiscale ...

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the ...