增强的随机处理

Version 10 expands on the already extensive random process framework with new processes, including hidden Markov models. Hidden Markov models are typically used to infer the hidden internal state from emissions, as in communication decoding, speech recognition, and biological sequence analysis. The random process framework also adds advanced time series processes and transformations of existing processes, as well as significantly improves computation with slice distributions—the bridge from random processes to random variables—often giving definite conclusions about expected process behavior from models.