Granger causality vs. dynamic Bayesian network inference: a
The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference
... As with any advanced statistical method, G-causality analy- sis via the MVGC toolbox should be implemented with care and with a good understanding of the underlying statistical principles and practical ...
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Analysing connectivity with Granger causality and dynamic causal modelling
... those describing the coupling among brain regions. These allow for model and system identification, respectively. DCM was introduced for fMRI timeseries [ 36 ], where the neuronal model comprises one or two hidden ...
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Intervention and causality: forecasting traffic flows using a dynamic Bayesian network
... use dynamic BNs to represent brand relationships when forecasting time series of brand ...a dynamic BN as regressors, when analysing a trial of cancer patients with liver ...
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Research Article DLI: A Deep Learning-Based Granger Causality Inference
... Deep learning-based architecture could learn more abstract representation from the input data without data stationarity requirement. Chong et al. [17] proposed a deep learning-based stock market forecasting model to ...
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Application of Granger causality to gene regulatory network discovery
... discovered network of 3 edges is obtained, which is shown in ...causal inference from Y to X is ...indirect inference through ...the inference “YX” is tested by conditioning on Z ...
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Granger causality in dynamic binary short panel data models
... tional inference, adapted to the present case, thereby taking care of the correlation between individual permanent unobserved heterogeneity and the model’s covariates as ...
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Granger causality in dynamic binary short panel data models
... tional inference, adapted to the present case, thereby taking care of the correlation between individual permanent unobserved heterogeneity and the model’s covariates as ...
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Investigating driver fatigue versus alertness using the Granger causality network
... form network structures, which are called Brain ...by Granger causality model, dynamic causality model and structural equation ...spectral Granger causality (GC), have ...
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MAP inference in dynamic hybrid Bayesian networks
... the dynamic MAP problem can be obtained using the HUGIN system ...exact inference in the unrolled network, and the approximate MAP sequence provided by our scheme can therefore be compared to the ...
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Applications of Granger causality to biological data
... fitted. Bayesian network is a graph-based model of joint multivariate probability distributions that cap- tures properties of conditional independence between variables, but as it re- quires a large number ...
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Granger-Causality in the presence of structural breaks
... the variables are not cointegrated and, moreover, are dominated by a deterministic trend, the F statistic does not converge to a stan- dard distribution and includes nuisance parameters. Furthermore, if the variables do ...
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Systemic Risk: Mapping of the Global Financial Network through Linear Granger Causality
... 4.2.2 Dynamic Model Let us move on the dynamic ...the network behaves over the years. In order to do that, we apply Granger causality test over 24-months rolling windows for a total of ...
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Visualisation Support for Biological Bayesian Network Inference
... of network inference (Figure ...for Bayesian net- work structure learning, called BANJO ...for dynamic Bayesian networks ...top-scoring network appearing ...sensus ...
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CiteSeerX — Dynamic Bayesian Networks: Representation, Inference and Learning
... genetic network topology using structural EM Here we describe some initial experiments using DBNs to learn small artificial examples typical of the causal processes involved in genetic ...
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Testing Granger Causality under Dynamic Covariance
... The Monte Carlo experiments show that the conventional test over-rejects the null hypothesis of non-causality, while the proposed test works satisfac- tory.. The propo[r] ...
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Network Granger Causality with Inherent Grouping Structure
... high-dimensional network models arises naturally in the analysis of many biological and socio-economic ...a network structure from temporal panel data, employing the framework of Granger causal ...
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Nonparametric estimation and inference for conditional density based Granger causality measures.
... and inference for conditional density based Granger causality mea- sures that quantify linear and nonlinear Granger ...the causality measures in terms of copula ...
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Granger causality revisited
... nonparametric inference based upon (surrogate) data in which directed temporal dependencies are ...to inference, it is worth noting that – from the perspective of DCM – inference rests on comparing ...
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Dynamic medium term Granger causality between growth and poverty
... traditional Granger causality tests to suit the short times series that are available, and use panel data model evaluation techniques to test the out-of-sample forecasting performance of competing ...a ...
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Looking behind Granger causality
... of Granger causality is quite ...t Granger causes Y t ...t Granger causes Y t . If Z t is empty, we refer it to bivariate Granger causality, otherwise to multivariate ...
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