English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Stationary stochastic processes with independent increments, of which thePoisson process is a prominent example, are widely used to describe real worldevents. With the basic assumption that a counting process is stationary and hasindependent increments, here I derive two integral equations to capture thetime evolution of any such process. In order to solve these two integralequations explicitly, I need to introduce one more restriction condition. Forsake of simplicity, I have imposed the Poisson condition on the two equationsand successfully reproduced the renown Poisson results. The methods proposedhere may also be applicable for investigating other stationary processes withindependent increments.

Integral Equation Approach to Stationary Stochastic Counting Process with Independent Increments