Reading Material

TOPIC 1: Georgi Dinolov, Multivariate dynamic models and matrix-variate dynamic models.

  • West and Harrison Chapter 16
  • Prado and West Chapter 10 (except material about Dynamic Graphical Models).

TOPIC 2: Devin Francom and Cheng-Han Yu, Software review

  • BSTS and DLM packages. Comparisons and examples. 
  • DLMs with INLA: https://www.math.ntnu.no/inla/r-inla.org/papers/Ruiz_Krainski_Rue_BA_23112010.pdf

TOPIC 3: Matt Heiner and Yifei Yu, Dynamic Graphical Models

  • Prado and West, Chapter 10 Sections 10.5.2, 10.6.6
  • Handout on graphical models (Duke): https://www2.stat.duke.edu/~km68/materials/214.10%20(Graphical%20Models).pdf
  • Jones, B., Carvalho, C., Dobra, A., Hans, C., Carter, C., & West, M. (2005). Experiments in stochastic computation for high-dimensional graphical models. Statistical Science, 388-400.
  • Carvalho and West (2007) Dynamic matrix-variate graphical models. Bayesian Analysis 2(1). 
  • Gruber, L. F. and M. West (2015). GPU-accelerated Bayesian learning and forecasting in simultaneous graphical dynamic linear models. Bayesian Analysis. Advance Publication, 2 March 2015

TOPIC 4: Dynamic latent threshold models 

  • Nakajima and West (2013) Bayesian dynamic factor models: Latent threshold approach. Journal of Financial Econometrics. 
  • Nakajima and West (2013) Bayesian analysis of latent threshold dynamic models. Journal of Business and Economic Statistics. 
  • Nakajima and West (2015) Dynamic network signal processing using latent threshold models 

TOPIC 5: Advances in Sequential Monte Carlo

  • Kantas et al (2015) On particle methods for parameter estimation in state space models: http://www.stats.ox.ac.uk/~doucet/kantas_doucet_singh_maciejowski_tutorialparameterestimation.pdf

  • http://www.stats.ox.ac.uk/%7Edoucet/poyiadjis_doucet_singh_particlescoreparameterestimation.pdf

TOPIC 6: Other, e.g., variable selection in time series, frequency-domain methods, applications to neuroscience