Summary
In this chapter, we summarized the technologies that we have exposed throughout this book. We have seen how to generate random numbers and listed the most frequently used algorithms for generating pseudo-random numbers. Then, we saw how to apply 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 resampling methods that are available. After that, we explored the most used numerical optimization techniques and learned how to use ANNs for numerical simulation.
Subsequently, we mentioned a series of fields in which numerical simulation is widely used and 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 in order to understand various methods...
