The analysis in this article does not use actual data obtained directly from note. I have used simulation data (n=500) generated in Python based on parameters set by the author using prior research ...

This repository is an example accompanying the DES RAP Book — an open educational resource on reproducible discrete-event simulation (DES) in Python and R. The book demonstrates best practices for ...

Operational risk modelling is a critical practice in financial institutions to quantify potential losses from internal processes, human errors, systems failures, or external events. One effective ...

The Poisson Distribution is a discrete probability distribution that models the number of events occurring within a fixed interval of time or space, provided these events happen independently and at a ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demo of Poisson regression, where the goal is to predict a count of things arriving, such as the number of telephone calls ...

Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process ...

StateSpaceDynamics.jl is a comprehensive and self-contained Julia package for working with probabilistic state space models (SSMs). It implements a wide range of state-space models, taking inspiration ...

Altogether, these results have two implications. First, scATAC-data carries information beyond binary accessibility. Second, fragment counts, but not read counts, can be more suitably modeled with the ...

The PoCA graphical user interface (GUI) is simple, intuitive and user friendly (Fig. 1b). It is designed to allow users as well as developers to perform efficient investigation and quantitative ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Many biochemical processes depend on the association of ...