Unraveling Markov Processes and Their Complexity

This chapter explores the fundamentals of Markov processes, highlighting the differences between IID draws and the dependency on prior states. It further investigates hidden Markov processes, using practical examples like weather patterns to explain the challenges of inferring hidden variables from observable data. The discussion also includes a comparison of hidden Markov machines to Turing machines, detailing their architecture and decision-making processes in information handling.