Finite mixture models and hidden Markov models (HMMs) occupy central roles in modern statistical inference and data analysis. Finite mixture models assume that data originate from a latent ...

Finite Mixture Models and Hidden Markov Models Publication Trend The graph below shows the total number of publications each year in Finite Mixture Models and Hidden Markov Models.

Drought is a naturally occurring climate phenomenon that significantly affects human and environmental activity, and can be considered one of the most widespread and destructive natural disasters.

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation ...

By merging online learning capabilities with the probabilistic framework of hidden Markov models, the OHMM can continuously update its geological risk predictions as new in situ observations ...

Definition A Hidden Markov Model (HMM) is a statistical model that assumes there are underlying, unobservable (hidden) states that drive observable outcomes.

“A Hidden Markov Model Combined with Climate Indices for Multi-decadal Streamflow Simulation,” Water Resources Research, 50, 7836-7846. Abstract: Hydroclimate time series often exhibit very low ...

During the making of an AI model, Performance metrics like accuracy, precision, recall, F1-score, ROC curves are used to ...