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 develop a new Bayesian modelling framework for the class of higher-order,variable-memory Markov chains, and introduce an associated collection ofmethodological tools for exact inference with discrete time series. We showthat a version of the context tree weighting algorithm can compute the priorpredictive likelihood exactly (averaged over both models and parameters), andtwo related algorithms are introduced, which identify the a posteriori mostlikely models and compute their exact posterior probabilities. All threealgorithms are deterministic and have linear-time complexity. A family ofvariable-dimension Markov chain Monte Carlo samplers is also provided,facilitating further exploration of the posterior. The performance of theproposed methods in model selection, Markov order estimation and prediction isillustrated through simulation experiments and real-world applications withdata from finance, genetics, neuroscience, and animal communication. Theassociated algorithms are implemented in the R package BCT.

Bayesian Context Trees: Modelling and exact inference for discrete time series