Bayesian Analysis

Dynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysis

Guy Freeman and Jim Q. Smith

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A new tree-based graphical model --- the dynamic staged tree --- is proposed for modelling discrete-valued discrete-time multivariate processes which are hypothesised to exhibit symmetries in how some intermediate situations might unfold. We define and implement a one-step-ahead prediction algorithm with the model using multi-process modelling and the power steady model that is robust to short-term variations in the data yet sensitive to underlying system changes. We demonstrate that the whole analysis can be performed in a conjugate way so that the potentially vast model space can be traversed quickly and then results communicated transparently. We also demonstrate how to analyse a general set of causal hypotheses on this model class. Our techniques are illustrated using a simple educational example.

Article information

Bayesian Anal., Volume 6, Number 2 (2011), 279-305.

First available in Project Euclid: 13 June 2012

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 05C90: Applications [See also 68R10, 81Q30, 81T15, 82B20, 82C20, 90C35, 92E10, 94C15] 62-09: Graphical methods 62F15: Bayesian inference 62P99: None of the above, but in this section 68T30: Knowledge representation

Staged trees graphical models Bayesian model selection Dirichlet distribution Bayes factors forecasting discrete time series causal inference power steady model multi-process model clustering


Freeman, Guy; Smith, Jim Q. Dynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysis. Bayesian Anal. 6 (2011), no. 2, 279--305. doi:10.1214/11-BA610.

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