Continuous-Time Latent Markov Factor Analysis for Exploring Measurement Model Changes Across Time

Model Checking Continuous-Time Markov Chains by Transient Analysis

The verification of continuous-time Markov chains (CTMCs) against continuous stochastic logic (CSL) [3, 6], a stochastic branchingtime temporal logic, is considered. CSL facilitates among others the specification of steady-state properties and the specification of probabilistic timing properties of the form P⊲⊳p(Φ1 U I Φ2), for state formulas Φ1 and Φ2, comparison operator ⊲⊳, probability p, an...

Continuous Time Markov Chains

Discrete-time Markov chains are useful in simulation, since updating algorithms are easier to construct in discrete steps. They can also be useful as crude models of physical, biological, and social processes. However, in the physical and biological worlds time runs continuously, and so discrete-time mathematical models are not always appropriate. This is especially true in population biology –...

Continuous Time Markov Networks

A central task in many applications is reasoning about processes that change over continuous time. Recently, Nodelman et al. introduced continuous time Bayesian networks (CTBNs), a structured representation for representing Continuous Time Markov Processes over a structured state space. In this paper, we introduce continuous time Markov networks (CTMNs), an alternative representation language t...

Continuous-time Markov Chains

A latent markov model for the analysis of longitudinal data collected in continuous time: states, durations, and transitions.

Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent Markov models to allow for the analysis of rating da...