A principled basis for nonequilibrium network flows. [PDF]
Nat CommunYang YJ, Dill KA.
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Markov Chains and Markov Processes
, 2016 Markov chain, which was named after Andrew Markov is a mathematical system that transfers a state to another state. Many real world systems contain uncertainty. This study helps us to understand the basic idea of a Markov chain and how is been useful in our daily lives.
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Journal of Forecasting, EarlyView.ABSTRACT This paper adopts a bivariate Markov‐switching multifractal (BMSM) model to reexamine comovement in SV between commodity, foreign exchange (FX), and stock markets. After the 2007–2008 global financial crisis understanding volatility linkages and the correlation structure between these markets becomes very important for risk analysts, portfolio
Ruipeng Liu +3 more
wiley +1 more source
Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models. [PDF]
IEEE Control Syst LettCramer E, Heiser LM, Chang YH.
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Nowcasting World Trade With Machine Learning: A Three‐Step Approach
Journal of Forecasting, EarlyView.ABSTRACT We nowcast world trade using machine learning, distinguishing between tree‐based methods (random forest and gradient boosting) and their linear‐regression‐based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the tree‐based techniques ...
Menzie Chinn +2 more
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Modeling Peak Expiratory Flow in Patients With Asthma and Quantifying Treatment Effects Using a Mixed-Effects Hidden Markov Model. [PDF]
CPT Pharmacometrics Syst PharmacolJakobsson L +4 more
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A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting
Journal of Forecasting, EarlyView.ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
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Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks. [PDF]
NPJ Syst Biol ApplPark KH, Albert R.
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Multivariate Markov Families of Copulas
Dependence Modeling, 2015 Overbeck Ludger, Schmidt Wolfgang M.
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Detecting phylogenetic signal in mutualistic interaction networks using a Markov process model. [PDF]
Oikos, 2014 Minoarivelo HO +4 more
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