Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network

DOI10.1198/jasa.2009.0042zbMath1388.62266OpenAlexW2080180737WikidataQ57977008 ScholiaQ57977008MaRDI QIDQ5252133

Catriona M. Queen, Casper J. Albers

Publication date: 29 May 2015

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://oro.open.ac.uk/17545/1/jasa%23A08-028R.pdf

zbMATH Keywords

multivariate time-seriesmultiregression dynamical modeldynamical linear model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics (62P99) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (10)

Searching multiregression dynamic models of resting-state fMRI networks using integer programming ⋮ Canonical analysis: ranks, ratios and fits ⋮ A group analysis using the multiregression dynamic models for fMRI networked time series ⋮ Studying the effective brain connectivity using multiregression dynamic models ⋮ Toward a Multisubject Analysis of Neural Connectivity ⋮ Distributional Kalman filters for Bayesian forecasting and closed form recurrences ⋮ Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models, Splines and Other Traffic Variables ⋮ Customized structural elicitation ⋮ Bayesian estimation of subset threshold autoregressions: short-term forecasting of traffic occupancy ⋮ Two Applications of Statistics to Traffic Models

This page was built for publication: Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network