This thesis explores compound Poisson processes, which extend the classical Poisson process by enabling the description of not only the number of events occurring over time but also the intensity associated with each individual event. After a brief review of key probability concepts, the work introduces Lévy processes, followed by an analysis of the fundamental properties of the Poisson process. The core of this study focuses on the compound Poisson process, presenting its formal definition and primary characteristics. Finally, several simulation examples are illustrated to demonstrate how this model is uniquely suited to describing real-world phenomena characterized by random, discrete events.
In questa tesi si studiano i processi di Poisson composti, che estendono il processo di Poisson classico permettendo di descrivere non solo il numero di eventi che si verificano nel tempo, ma anche l’intensità associata a ciascun evento. Dopo un breve richiamo dei principali concetti di probabilità, vengono introdotti i processi di Lévy e poi il processo di Poisson di cui ne vengono analizzate le proprietà fondamentali. Il lavoro si concentra maggiormente sul processo di Poisson composto, in cui vengono presentate definizione e principali caratteristiche. Infine, vengono illustrati alcuni esempi di simulazione per mostrare come questo modello sia adatto a descrivere fenomeni reali caratterizzati da eventi casuali.
Processi di Poisson composti
GAGLIANO, NICOLA
2025/2026
Abstract
This thesis explores compound Poisson processes, which extend the classical Poisson process by enabling the description of not only the number of events occurring over time but also the intensity associated with each individual event. After a brief review of key probability concepts, the work introduces Lévy processes, followed by an analysis of the fundamental properties of the Poisson process. The core of this study focuses on the compound Poisson process, presenting its formal definition and primary characteristics. Finally, several simulation examples are illustrated to demonstrate how this model is uniquely suited to describing real-world phenomena characterized by random, discrete events.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/112190