A geometric Brownian motion (GBM) is a continuous-time stochastic process in which the logarithm of the random variables follows a Brownian motion (also called a Wiener process) with drift. It is an ...

Stochastic processes are at the center of probability theory, both from a theoretical and an applied viewpoint. Stochastic processes have applications in many disciplines such as physics, computer ...

Abstract: This chapter introduces several stochastic models largely used to describe traffic processes. It is concerned with point processes. With reference to the unidimensional real line, often ...

This package offers a number of common discrete-time, continuous-time, and noise process objects for generating realizations of stochastic processes as numpy arrays. The diffusion processes are ...

A research team led by Prof. Wang Jianjun from the Nanjing Institute of Geography and Limnology of the Chinese Academy of Sciences, has produced a global map depicting the distribution and variation ...

The classical Melnikov method provides information on the behavior of deterministic planar systems that may exhibit transitions, i.e. escapes from and captures into preferred regions of phase space.