High-order Markov chain models extend the conventional framework by incorporating dependencies that span several previous states rather than solely the immediate past. This extension allows for a ...

Discover how Markov chains predict real systems, from Ulam and von Neumann’s Monte Carlo to PageRank, so you can grasp ...

Discrete-time hidden Markov models are a broadly useful class of latent variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common ...

Until recently, Markov models and analytical methods were fairly obscure mathematical techniques rarely applied outside of academic settings. The advent of functional safety standards, particularly ...