Latent Dirichlet Allocation Coupled with Time Series Analyses //
Combines Latent Dirichlet Allocation (LDA) and Bayesian
multinomial time series methods in a two-stage analysis to
quantify dynamics in high-dimensional temporal data. LDA
decomposes multivariate data into lower-dimension latent
groupings, whose relative proportions are modeled using
generalized Bayesian time series models that include abrupt
changepoints and smooth dynamics. The methods are described in
Blei et al. (2003) doi:10.1162/jmlr.2003.3.4-5.993, Western and
Kleykamp (2004) doi:10.1093/pan/mph023, Venables and Ripley
(2002, ISBN-13:978-0387954578), and Christensen et al. (2018)
doi:10.1002/ecy.2373.