Markov chains provide a fundamental framework for modelling stochastic processes, where the next state depends solely on the current state. Hidden Markov models (HMMs) extend this framework by ...
Discover how Markov chains predict real systems, from Ulam and von Neumann’s Monte Carlo to PageRank, so you can grasp ...
Probabilistic model checking and Markov decision processes (MDPs) form two interlinked branches of formal analysis for systems operating under uncertainty. These techniques offer a mathematical ...
Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov ...
Introduction Integrating smoking cessation supports into lung cancer screening can improve abstinence rates. However, healthcare decision-makers need evidence of cost-effectiveness to understand the ...
Attrition bias: Does the benefit of targeted agents (TA) increase the more we search for a selection biomarker? Meta-regression analysis of randomized clinical trials (RCTs) in advanced non-small cell ...
BACKGROUND: The FINE-CKD model was developed to estimate the cost-effectiveness of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). OBJECTIVE: To perform internal ...
Patient-reported pain and other symptoms as prognostic factors for overall survival (OS) in a phase III clinical trial of patients with advanced breast cancer. This is an ASCO Meeting Abstract from ...
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