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 ...
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 ...
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 ...
A 30-minute talk about Markov modeling generally, with specific reference to the seminal 1986 contribution of Professor Eaves, which described Markov processes for genetic and environmental variance ...
In this paper, a Markov-switching linked autoregressive model is proposed to describe and forecast non-continuous wind direction data. Due to the influence factors of geography and atmosphere, the ...
Antitumor activity of MP0250, a bispecific VEGF- and HGF-targeting darpin, in patient-derived xenograft models. This is an ASCO Meeting Abstract from the 2014 ASCO Annual Meeting I. This abstract does ...
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