Some interesting applications of Stochastic Processes using Jupyter Notebooks for descriptive and instructive illustrations are included in this repository and can be summarized in the following list:

1. Markov Chains
2. Random Walks
3. Markov Chain Monte Carlo (MCMC) Sampling
4. Monte Carlo Approximations
5. Ergodic Theorem
6. Ising Model
7. Travelling Salesman Problem

Modelling a tennis match using Markov Chains.

Two dimensional Random Walk.

Approximating the value of 𝛑 using Monte Carlo Estimates.

Monte Carlo simulation of the two dimensional Ising Model.

Solving the Travelling Salesman Problem usng the Simulated Annealing Algorithm.

[1] https://repository.kallipos.gr/handle/11419/6003

[2] http://math.ntua.gr/~loulakis/info/python_codes_files