Objectives This study aims to explore the dynamics of neurological functional disability in patients with intracerebral haemorrhage (ICH) using a multistate Markov model and to investigate the factors ...

PyEMMA (EMMA = Emma's Markov Model Algorithms) is an open source Python/C package for analysis of extensive molecular dynamics simulations. In particular, it includes algorithms for estimation, ...

The incremental cost-effectiveness ratio (ICER) of the traditional tuberculin skin test (TST) strategy was significantly lower than the willingness-to-pay threshold, indicating its economic advantage.

The amino acid sequence of the transmembrane protein and its corresponding positions on the cell membrane are transformed into a hidden Markov process. After evaluating the parameters, the Viterbi ...

Hidden Markov models (HMMs) provide a robust statistical framework for analysing sequential data by assuming that the observed processes are driven by underlying, unobserved states. These models have ...

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 ...

Abstract: We use Markov categories to generalize the basic theory of Markov chains and hidden Markov models to an abstract setting. This comprises characterizations of hidden Markov models in terms of ...

This package implements computational models for analyzing choice behavior using mixture-of-agents frameworks. The core innovation is decomposing complex decision-making into interpretable cognitive ...

This paper is concerned with the computational complexity of learning the Hidden Markov Model (HMM). Although HMMs are some of the most widely used tools in sequential and time series modeling, they ...