STOCHASTIC PROCESSES HOW TO

This is the reason why today Chaos Theory is sometimes referred to as “Butterfly Effect”. “A butterfly flapping its wings in Brazil can produce a tornado in Texas” What Lorentz noticed during his analysis, was that even small perturbation in the atmosphere can cause climate changes.Ī famous expression used by Lorenz to describe this behaviour is:

STOCHASTIC PROCESSES HOW TO

At that time, Lorentz was working on how to improve weather forecasting. The study of chaotic systems came to a breakthrough in 1963 thanks to Edward Norton Lorenz. Nowadays, this kind of behaviour is known as Deterministic Chaos and it is a well-distinct ambit from Genuine Randomness.

During the last century, many mathematics such as Poincare, Lorentz and Turing have been fascinated and intrigued by this topic. What makes stochastic processes so special, is their dependence on the model initial condition. Stochastic processes are part of our daily life. In this article, I will briefly introduce you to each of these processes.

Auto-Regressive and Moving average processes: employed in time-series analysis (eg.

Hyper-Parameters tuning and Automated Machine Learning).

Gaussian Processes: used in regression and optimisation problems (eg.

Markov decision processes: commonly used in Computational Biology and Reinforcement Learning.

Random Walk and Brownian motion processes: used in algorithmic trading.

Poisson processes: for dealing with waiting times and queues.

Some examples of stochastic processes used in Machine Learning are: One of the main application of Machine Learning is modelling stochastic processes.