English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a flexible nonparametric Bayesian modelling framework formultivariate time series of count data based on tensor factorisations. Ourmodels can be viewed as infinite state space Markov chains of known maximalorder with non-linear serial dependence through the introduction of appropriatelatent variables. Alternatively, our models can be viewed as Bayesianhierarchical models with conditionally independent Poisson distributedobservations. Inference about the important lags and their complex interactionsis achieved via MCMC. When the observed counts are large, we deal with theresulting computational complexity of Bayesian inference via a two-stepinferential strategy based on an initial analysis of a training set of thedata. Our methodology is illustrated using simulation experiments and analysisof real-world data.

Bayesian Tensor Factorisations for Time Series of Counts