wesgraham / hawkes_markovprocesses

Jupyter Notebook for Simulating Hawkes Processes

Hawkes_MarkovProcesses

Python Script for Simulating Hawkes and Markov Processes

About Hawkes Processes

Hawkes processes are a particularly interesting class of stochastic process that have been applied in diverse areas, from earthquake modelling to financial analysis. They are point processes whose defining characteristic is that they 'self-excite', meaning that each arrival increases the rate of future arrivals for some period of time. Hawkes processes are well established, particularly within the financial literature, yet many of the treatments are inaccessible to one not acquainted with the topic.

This type of process is used to model self-exciting point processes, for example - how many tweets one could expect in the event of a major news event, the buying order tendencies displayed once a whale purchases a large lump sum of stock, and more.

About Markov Decision Processes

A Markov decision process (MDP) is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. Source: Wikipedia