Summary
In this chapter, we summarized the technologies that we have exposed throughout this book. We have seen how to generate random numbers and have listed the most frequently used algorithms used to generate pseudo-random numbers. Then, we saw how to apply the Monte Carlo methods for numerical simulation based on the assumptions of two fundamental laws: the law of large numbers and the central limit theorem. We then went on to summarize the concepts that Markovian models are based on and then analyzed the various available resampling methods. After that, we explored the most used numerical optimization techniques and learned how to use artificial neural networks for numerical simulation.
Subsequently, we mentioned a series of fields in which numerical simulation is widely used and then looked at the next steps that will allow simulation models to evolve.
In this book, we studied various computational statistical simulations using Python. We started with the basics to understand...