rSGDLM

rSGDLM is an R package that implements the Variational Bayes method for online learning of the Simultaneous Graphical DLM presented in GPU-Accelerated Bayesian Learning and Forecasting in Simultaneous Graphical Dynamic Linear Models (https://doi.org/10.1214/15-BA946). A method for online selection of the simultaneous parental sets is described in Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models (https://doi.org/10.1016/j.ecosta.2017.03.003).


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Fisher, Jared D.; Pettenuzzo, Davide; Carvalho, Carlos M.: Optimal asset allocation with multivariate Bayesian dynamic linear models (2020)
  2. McAlinn, Kenichiro; Aastveit, Knut Are; Nakajima, Jouchi; West, Mike: Multivariate Bayesian predictive synthesis in macroeconomic forecasting (2020)
  3. West, Mike: Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions (2020)
  4. Crawford, Lorin; Flaxman, Seth R.; Runcie, Daniel E.; West, Mike: Variable prioritization in nonlinear black box methods: a genetic association case study (2019)
  5. Gruber, Lutz F.; Stuber, Erica F.; Wszola, Lyndsie S.; Fontaine, Joseph J.: Estimating the use of public lands: integrated modeling of open populations with convolution likelihood ecological abundance regression (2019)
  6. Irie, Kaoru; West, Mike: Bayesian emulation for multi-step optimization in decision problems (2019)
  7. Kastner, Gregor: Sparse Bayesian time-varying covariance estimation in many dimensions (2019)
  8. McAlinn, Kenichiro; West, Mike: Dynamic Bayesian predictive synthesis in time series forecasting (2019)
  9. Ling, Hui `Fox’; Franzen, Christian: Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis (2017)
  10. Gruber, Lutz; West, Mike: GPU-accelerated Bayesian learning and forecasting in simultaneous graphical dynamic linear models (2016)