Journal article

Minimum message length analysis of multiple short time series

Daniel F Schmidt, Enes Makalic

Statistics & Probability Letters | ELSEVIER SCIENCE BV | Published : 2016

DOI: 10.1016/j.spl.2015.09.021

University of Melbourne Researchers

Enes Makalic Author Melbourne School of Population and Global Health

Daniel Schmidt Author Melbourne School of Population and Global Health

Keywords

Bayesian Inference

Nuisance Parameters

Approximate Conditional Likelihood

Physical Sciences

Statistics & Probability

Gaussian Autoregressive Processes

Minimum Message Length

Science & Technology